COMPOSITION
DESIGN
COLOR
-
Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color picking
Read more: Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color pickinghttps://bottosson.github.io/misc/colorpicker
https://bottosson.github.io/posts/colorpicker/
https://www.smashingmagazine.com/2024/10/interview-bjorn-ottosson-creator-oklab-color-space/
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
-
PBR Color Reference List for Materials – by Grzegorz Baran
Read more: PBR Color Reference List for Materials – by Grzegorz Baran“The list should be helpful for every material artist who work on PBR materials as it contains over 200 color values measured with PCE-RGB2 1002 Color Spectrometer device and presented in linear and sRGB (2.2) gamma space.
All color values, HUE and Saturation in this list come from measurements taken with PCE-RGB2 1002 Color Spectrometer device and are presented in linear and sRGB (2.2) gamma space (more info at the end of this video) I calculated Relative Luminance and Luminance values based on captured color using my own equation which takes color based luminance perception into consideration. Bare in mind that there is no ‘one’ color per substance as nothing in nature is even 100% uniform and any value in +/-10% range from these should be considered as correct one. Therefore this list should be always considered as a color reference for material’s albedos, not ulitimate and absolute truth.“
-
Gamma correction
Read more: Gamma correctionhttp://www.normankoren.com/makingfineprints1A.html#Gammabox
https://en.wikipedia.org/wiki/Gamma_correction
http://www.photoscientia.co.uk/Gamma.htm
https://www.w3.org/Graphics/Color/sRGB.html
http://www.eizoglobal.com/library/basics/lcd_display_gamma/index.html
https://forum.reallusion.com/PrintTopic308094.aspx
Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships.
Three main types:
– Image Gamma encoded in images
– Display Gammas encoded in hardware and/or viewing time
– System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.Our eyes, different camera or video recorder devices do not correctly capture luminance. (they are not linear)
Different display devices (monitor, phone screen, TV) do not display luminance correctly neither. So, one needs to correct them, therefore the gamma correction function.The human perception of brightness, under common illumination conditions (not pitch black nor blindingly bright), follows an approximate power function (note: no relation to the gamma function), with greater sensitivity to relative differences between darker tones than between lighter ones, consistent with the Stevens’ power law for brightness perception. If images are not gamma-encoded, they allocate too many bits or too much bandwidth to highlights that humans cannot differentiate, and too few bits or too little bandwidth to shadow values that humans are sensitive to and would require more bits/bandwidth to maintain the same visual quality.
https://blog.amerlux.com/4-things-architects-should-know-about-lumens-vs-perceived-brightness/
cones manage color receptivity, rods determine how large our pupils should be. The larger (more dilated) our pupils are, the more light enters our eyes. In dark situations, our rods dilate our pupils so we can see better. This impacts how we perceive brightness.
https://www.cambridgeincolour.com/tutorials/gamma-correction.htm
A gamma encoded image has to have “gamma correction” applied when it is viewed — which effectively converts it back into light from the original scene. In other words, the purpose of gamma encoding is for recording the image — not for displaying the image. Fortunately this second step (the “display gamma”) is automatically performed by your monitor and video card. The following diagram illustrates how all of this fits together:
Display gamma
The display gamma can be a little confusing because this term is often used interchangeably with gamma correction, since it corrects for the file gamma. This is the gamma that you are controlling when you perform monitor calibration and adjust your contrast setting. Fortunately, the industry has converged on a standard display gamma of 2.2, so one doesn’t need to worry about the pros/cons of different values.Gamma encoding of images is used to optimize the usage of bits when encoding an image, or bandwidth used to transport an image, by taking advantage of the non-linear manner in which humans perceive light and color. Human response to luminance is also biased. Especially sensible to dark areas.
Thus, the human visual system has a non-linear response to the power of the incoming light, so a fixed increase in power will not have a fixed increase in perceived brightness.
We perceive a value as half bright when it is actually 18% of the original intensity not 50%. As such, our perception is not linear.You probably already know that a pixel can have any ‘value’ of Red, Green, and Blue between 0 and 255, and you would therefore think that a pixel value of 127 would appear as half of the maximum possible brightness, and that a value of 64 would represent one-quarter brightness, and so on. Well, that’s just not the case.
Pixar Color Management
https://renderman.pixar.com/color-management
– Why do we need linear gamma?
Because light works linearly and therefore only works properly when it lights linear values.– Why do we need to view in sRGB?
Because the resulting linear image in not suitable for viewing, but contains all the proper data. Pixar’s IT viewer can compensate by showing the rendered image through a sRGB look up table (LUT), which is identical to what will be the final image after the sRGB gamma curve is applied in post.This would be simple enough if every software would play by the same rules, but they don’t. In fact, the default gamma workflow for many 3D software is incorrect. This is where the knowledge of a proper imaging workflow comes in to save the day.
Cathode-ray tubes have a peculiar relationship between the voltage applied to them, and the amount of light emitted. It isn’t linear, and in fact it follows what’s called by mathematicians and other geeks, a ‘power law’ (a number raised to a power). The numerical value of that power is what we call the gamma of the monitor or system.
Thus. Gamma describes the nonlinear relationship between the pixel levels in your computer and the luminance of your monitor (the light energy it emits) or the reflectance of your prints. The equation is,
Luminance = C * value^gamma + black level
– C is set by the monitor Contrast control.
– Value is the pixel level normalized to a maximum of 1. For an 8 bit monitor with pixel levels 0 – 255, value = (pixel level)/255.
– Black level is set by the (misnamed) monitor Brightness control. The relationship is linear if gamma = 1. The chart illustrates the relationship for gamma = 1, 1.5, 1.8 and 2.2 with C = 1 and black level = 0.
Gamma affects middle tones; it has no effect on black or white. If gamma is set too high, middle tones appear too dark. Conversely, if it’s set too low, middle tones appear too light.
The native gamma of monitors– the relationship between grid voltage and luminance– is typically around 2.5, though it can vary considerably. This is well above any of the display standards, so you must be aware of gamma and correct it.
A display gamma of 2.2 is the de facto standard for the Windows operating system and the Internet-standard sRGB color space.
The old standard for Mcintosh and prepress file interchange is 1.8. It is now 2.2 as well.
Video cameras have gammas of approximately 0.45– the inverse of 2.2. The viewing or system gamma is the product of the gammas of all the devices in the system– the image acquisition device (film+scanner or digital camera), color lookup table (LUT), and monitor. System gamma is typically between 1.1 and 1.5. Viewing flare and other factor make images look flat at system gamma = 1.0.
Most laptop LCD screens are poorly suited for critical image editing because gamma is extremely sensitive to viewing angle.
More about screens
https://www.cambridgeincolour.com/tutorials/gamma-correction.htm
CRT Monitors. Due to an odd bit of engineering luck, the native gamma of a CRT is 2.5 — almost the inverse of our eyes. Values from a gamma-encoded file could therefore be sent straight to the screen and they would automatically be corrected and appear nearly OK. However, a small gamma correction of ~1/1.1 needs to be applied to achieve an overall display gamma of 2.2. This is usually already set by the manufacturer’s default settings, but can also be set during monitor calibration.
LCD Monitors. LCD monitors weren’t so fortunate; ensuring an overall display gamma of 2.2 often requires substantial corrections, and they are also much less consistent than CRT’s. LCDs therefore require something called a look-up table (LUT) in order to ensure that input values are depicted using the intended display gamma (amongst other things). See the tutorial on monitor calibration: look-up tables for more on this topic.
About black level (brightness). Your monitor’s brightness control (which should actually be called black level) can be adjusted using the mostly black pattern on the right side of the chart. This pattern contains two dark gray vertical bars, A and B, which increase in luminance with increasing gamma. (If you can’t see them, your black level is way low.) The left bar (A) should be just above the threshold of visibility opposite your chosen gamma (2.2 or 1.8)– it should be invisible where gamma is lower by about 0.3. The right bar (B) should be distinctly visible: brighter than (A), but still very dark. This chart is only for monitors; it doesn’t work on printed media.
The 1.8 and 2.2 gray patterns at the bottom of the image represent a test of monitor quality and calibration. If your monitor is functioning properly and calibrated to gamma = 2.2 or 1.8, the corresponding pattern will appear smooth neutral gray when viewed from a distance. Any waviness, irregularity, or color banding indicates incorrect monitor calibration or poor performance.
Another test to see whether one’s computer monitor is properly hardware adjusted and can display shadow detail in sRGB images properly, they should see the left half of the circle in the large black square very faintly but the right half should be clearly visible. If not, one can adjust their monitor’s contrast and/or brightness setting. This alters the monitor’s perceived gamma. The image is best viewed against a black background.
This procedure is not suitable for calibrating or print-proofing a monitor. It can be useful for making a monitor display sRGB images approximately correctly, on systems in which profiles are not used (for example, the Firefox browser prior to version 3.0 and many others) or in systems that assume untagged source images are in the sRGB colorspace.
On some operating systems running the X Window System, one can set the gamma correction factor (applied to the existing gamma value) by issuing the command xgamma -gamma 0.9 for setting gamma correction factor to 0.9, and xgamma for querying current value of that factor (the default is 1.0). In OS X systems, the gamma and other related screen calibrations are made through the System Preference
https://www.kinematicsoup.com/news/2016/6/15/gamma-and-linear-space-what-they-are-how-they-differ
Linear color space means that numerical intensity values correspond proportionally to their perceived intensity. This means that the colors can be added and multiplied correctly. A color space without that property is called ”non-linear”. Below is an example where an intensity value is doubled in a linear and a non-linear color space. While the corresponding numerical values in linear space are correct, in the non-linear space (gamma = 0.45, more on this later) we can’t simply double the value to get the correct intensity.
The need for gamma arises for two main reasons: The first is that screens have been built with a non-linear response to intensity. The other is that the human eye can tell the difference between darker shades better than lighter shades. This means that when images are compressed to save space, we want to have greater accuracy for dark intensities at the expense of lighter intensities. Both of these problems are resolved using gamma correction, which is to say the intensity of every pixel in an image is put through a power function. Specifically, gamma is the name given to the power applied to the image.
CRT screens, simply by how they work, apply a gamma of around 2.2, and modern LCD screens are designed to mimic that behavior. A gamma of 2.2, the reciprocal of 0.45, when applied to the brightened images will darken them, leaving the original image.
-
What causes color
www.webexhibits.org/causesofcolor/5.html
Water itself has an intrinsic blue color that is a result of its molecular structure and its behavior.
-
Pattern generators
Read more: Pattern generatorshttp://qrohlf.com/trianglify-generator/
https://halftonepro.com/app/polygons#
https://mattdesl.svbtle.com/generative-art-with-nodejs-and-canvas
https://www.patterncooler.com/
http://permadi.com/java/spaint/spaint.html
https://dribbble.com/shots/1847313-Kaleidoscope-Generator-PSD
http://eskimoblood.github.io/gerstnerizer/
http://www.stripegenerator.com/
http://btmills.github.io/geopattern/geopattern.html
http://fractalarchitect.net/FA4-Random-Generator.html
https://sciencevsmagic.net/fractal/#0605,0000,3,2,0,1,2
https://sites.google.com/site/mandelbulber/home
-
Tobia Montanari – Memory Colors: an essential tool for Colorists
Read more: Tobia Montanari – Memory Colors: an essential tool for Coloristshttps://www.tobiamontanari.com/memory-colors-an-essential-tool-for-colorists/
“Memory colors are colors that are universally associated with specific objects, elements or scenes in our environment. They are the colors that we expect to see in specific situations: these colors are based on our expectation of how certain objects should look based on our past experiences and memories.
For instance, we associate specific hues, saturation and brightness values with human skintones and a slight variation can significantly affect the way we perceive a scene.
Similarly, we expect blue skies to have a particular hue, green trees to be a specific shade and so on.
Memory colors live inside of our brains and we often impose them onto what we see. By considering them during the grading process, the resulting image will be more visually appealing and won’t distract the viewer from the intended message of the story. Even a slight deviation from memory colors in a movie can create a sense of discordance, ultimately detracting from the viewer’s experience.”
-
Victor Perez – The Color Management Handbook for Visual Effects Artists
Read more: Victor Perez – The Color Management Handbook for Visual Effects ArtistsDigital Color Principles, Color Management Fundamentals & ACES Workflows
-
Weta Digital – Manuka Raytracer and Gazebo GPU renderers – pipeline
Read more: Weta Digital – Manuka Raytracer and Gazebo GPU renderers – pipelinehttps://jo.dreggn.org/home/2018_manuka.pdf
http://www.fxguide.com/featured/manuka-weta-digitals-new-renderer/
The Manuka rendering architecture has been designed in the spirit of the classic reyes rendering architecture. In its core, reyes is based on stochastic rasterisation of micropolygons, facilitating depth of field, motion blur, high geometric complexity,and programmable shading.
This is commonly achieved with Monte Carlo path tracing, using a paradigm often called shade-on-hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure which is then used bypath sampling logic to evaluate contributions and to inform what further rays to cast through the scene.
Over the years, however, the expectations have risen substantially when it comes to image quality. Computing pictures which are indistinguishable from real footage requires accurate simulation of light transport, which is most often performed using some variant of Monte Carlo path tracing. Unfortunately this paradigm requires random memory accesses to the whole scene and does not lend itself well to a rasterisation approach at all.
Manuka is both a uni-directional and bidirectional path tracer and encompasses multiple importance sampling (MIS). Interestingly, and importantly for production character skin work, it is the first major production renderer to incorporate spectral MIS in the form of a new ‘Hero Spectral Sampling’ technique, which was recently published at Eurographics Symposium on Rendering 2014.
Manuka propose a shade-before-hit paradigm in-stead and minimise I/O strain (and some memory costs) on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling, “compressing” any bvh structure as needed, and as such also limiting duplication of source data.
The difference with reyes is that instead of baking colors into the geometry like in Reyes, manuka bakes surface closures. This means that light transport is still calculated with path tracing, but all texture lookups etc. are done up-front and baked into the geometry.The main drawback with this method is that geometry has to be tessellated to its highest, stable topology before shading can be evaluated properly. As such, the high cost to first pixel. Even a basic 4 vertices square becomes a much more complex model with this approach.
Manuka use the RenderMan Shading Language (rsl) for programmable shading [Pixar Animation Studios 2015], but we do not invoke rsl shaders when intersecting a ray with a surface (often called shade-on-hit). Instead, we pre-tessellate and pre-shade all the input geometry in the front end of the renderer.
This way, we can efficiently order shading computations to sup-port near-optimal texture locality, vectorisation, and parallelism. This system avoids repeated evaluation of shaders at the same surface point, and presents a minimal amount of memory to be accessed during light transport time. An added benefit is that the acceleration structure for ray tracing (abounding volume hierarchy, bvh) is built once on the final tessellated geometry, which allows us to ray trace more efficiently than multi-level bvhs and avoids costly caching of on-demand tessellated micropolygons and the associated scheduling issues.For the shading reasons above, in terms of AOVs, the studio approach is to succeed at combining complex shading with ray paths in the render rather than pass a multi-pass render to compositing.
For the Spectral Rendering component. The light transport stage is fully spectral, using a continuously sampled wavelength which is traced with each path and used to apply the spectral camera sensitivity of the sensor. This allows for faithfully support any degree of observer metamerism as the camera footage they are intended to match as well as complex materials which require wavelength dependent phenomena such as diffraction, dispersion, interference, iridescence, or chromatic extinction and Rayleigh scattering in participating media.
As opposed to the original reyes paper, we use bilinear interpolation of these bsdf inputs later when evaluating bsdfs per pathv ertex during light transport4. This improves temporal stability of geometry which moves very slowly with respect to the pixel raster
In terms of the pipeline, everything rendered at Weta was already completely interwoven with their deep data pipeline. Manuka very much was written with deep data in mind. Here, Manuka not so much extends the deep capabilities, rather it fully matches the already extremely complex and powerful setup Weta Digital already enjoy with RenderMan. For example, an ape in a scene can be selected, its ID is available and a NUKE artist can then paint in 3D say a hand and part of the way up the neutral posed ape.
We called our system Manuka, as a respectful nod to reyes: we had heard a story froma former ILM employee about how reyes got its name from how fond the early Pixar people were of their lunches at Point Reyes, and decided to name our system after our surrounding natural environment, too. Manuka is a kind of tea tree very common in New Zealand which has very many very small leaves, in analogy to micropolygons ina tree structure for ray tracing. It also happens to be the case that Weta Digital’s main site is on Manuka Street.
-
THOMAS MANSENCAL – The Apparent Simplicity of RGB Rendering
https://thomasmansencal.substack.com/p/the-apparent-simplicity-of-rgb-rendering
The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.
Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.
-
About green screens
Read more: About green screenshackaday.com/2015/02/07/how-green-screen-worked-before-computers/
www.newtek.com/blog/tips/best-green-screen-materials/
www.chromawall.com/blog//chroma-key-green
Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS).
Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production:
Green Screen as RGB Color Value: 0, 177, 64
Green Screen as CMYK Color Value: 81, 0, 92, 0
Green Screen as Hex Color Value: #00b140
Green Screen as Websafe Color Value: #009933Chroma Key Green is reasonably close to an 18% gray reflectance.
Illuminate your green screen with an uniform source with less than 2/3 EV variation.
The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting.
LIGHTING
-
Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesis
Read more: Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesishttps://srameo.github.io/projects/le3d/
LE3D is a method for real-time HDR view synthesis from RAW images. It is particularly effective for nighttime scenes.
https://github.com/Srameo/LE3D
-
Arto T. – A workflow for creating photorealistic, equirectangular 360° panoramas in ComfyUI using Flux
https://civitai.com/models/735980/flux-equirectangular-360-panorama
https://civitai.com/models/745010?modelVersionId=833115
The trigger phrase is “equirectangular 360 degree panorama”. I would avoid saying “spherical projection” since that tends to result in non-equirectangular spherical images.
Image resolution should always be a 2:1 aspect ratio. 1024 x 512 or 1408 x 704 work quite well and were used in the training data. 2048 x 1024 also works.
I suggest using a weight of 0.5 – 1.5. If you are having issues with the image generating too flat instead of having the necessary spherical distortion, try increasing the weight above 1, though this could negatively impact small details of the image. For Flux guidance, I recommend a value of about 2.5 for realistic scenes.
8-bit output at the moment
-
Is a MacBeth Colour Rendition Chart the Safest Way to Calibrate a Camera?
Read more: Is a MacBeth Colour Rendition Chart the Safest Way to Calibrate a Camera?www.colour-science.org/posts/the-colorchecker-considered-mostly-harmless/
“Unless you have all the relevant spectral measurements, a colour rendition chart should not be used to perform colour-correction of camera imagery but only for white balancing and relative exposure adjustments.”
“Using a colour rendition chart for colour-correction might dramatically increase error if the scene light source spectrum is different from the illuminant used to compute the colour rendition chart’s reference values.”
“other factors make using a colour rendition chart unsuitable for camera calibration:
– Uncontrolled geometry of the colour rendition chart with the incident illumination and the camera.
– Unknown sample reflectances and ageing as the colour of the samples vary with time.
– Low samples count.
– Camera noise and flare.
– Etc…“Those issues are well understood in the VFX industry, and when receiving plates, we almost exclusively use colour rendition charts to white balance and perform relative exposure adjustments, i.e. plate neutralisation.”
-
Open Source Nvidia Omniverse
Read more: Open Source Nvidia Omniverseblogs.nvidia.com/blog/2019/03/18/omniverse-collaboration-platform/
developer.nvidia.com/nvidia-omniverse
An open, Interactive 3D Design Collaboration Platform for Multi-Tool Workflows to simplify studio workflows for real-time graphics.
It supports Pixar’s Universal Scene Description technology for exchanging information about modeling, shading, animation, lighting, visual effects and rendering across multiple applications.
It also supports NVIDIA’s Material Definition Language, which allows artists to exchange information about surface materials across multiple tools.
With Omniverse, artists can see live updates made by other artists working in different applications. They can also see changes reflected in multiple tools at the same time.
For example an artist using Maya with a portal to Omniverse can collaborate with another artist using UE4 and both will see live updates of each others’ changes in their application.
-
What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…
RASTERIZATION
Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.
In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.
With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.
Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.
Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.
The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.
There are also multiple problems:
If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.
The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.
You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)
Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)
RAY CASTING
It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.
You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.
RAYTRACING
Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.
Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.
Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.
Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.
PATH TRACING
Path tracing can be used to solve more complex lighting situations.
Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.
Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/
https://en.wikipedia.org/wiki/Rasterisation
https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing
COLLECTIONS
| Featured AI
| Design And Composition
| Explore posts
POPULAR SEARCHES
unreal | pipeline | virtual production | free | learn | photoshop | 360 | macro | google | nvidia | resolution | open source | hdri | real-time | photography basics | nuke
FEATURED POSTS
Social Links
DISCLAIMER – Links and images on this website may be protected by the respective owners’ copyright. All data submitted by users through this site shall be treated as freely available to share.
