First introduced in 2012, nowadays OpenVDB is commonly applied in simulation tools such as Houdini, EmberGen, Blender, and used in feature film production for creating realistic volumetric images. This format, however, lacks the GPUs support and can not be applied in games due to the considerable file size (on average at least a few Gigabytes) and computational effort required to render 3D volumes.
Volumetric data has numerous important applications in computer graphics and VFX production. It’s used for volume rendering, fluid simulation, fracture simulation, modeling with implicit surfaces, etc. However, this data is not so easy to work with. In most cases volumetric data is represented on spatially uniform, regular 3D grids. Although dense regular grids are convenient for several reasons, they have one major drawback – their memory footprint grows cubically with respect to grid resolution.
OpenVDB format, developed by DreamWorksAnimation, partially solves this issue by storing voxel data in a tree-like data structure that allows the creation of sparse volumes. The beauty behind this system is that it completely ignores empty cells, which drastically decreases memory and disk usage, simultaneously making the rendering of volumes much faster.
The EU Artificial Intelligence (AI) Act, which went into effect on August 1, 2024.
This act implements a risk-based approach to AI regulation, categorizing AI systems based on the level of risk they pose. High-risk systems, such as those used in healthcare, transport, and law enforcement, face stringent requirements, including risk management, transparency, and human oversight.
Key provisions of the AI Act include:
Transparency and Safety Requirements: AI systems must be designed to be safe, transparent, and easily understandable to users. This includes labeling requirements for AI-generated content, such as deepfakes (Engadget).
Risk Management and Compliance: Companies must establish comprehensive governance frameworks to assess and manage the risks associated with their AI systems. This includes compliance programs that cover data privacy, ethical use, and geographical considerations (Faegre Drinker Biddle & Reath LLP) (Passle).
Copyright and Data Mining: Companies must adhere to copyright laws when training AI models, obtaining proper authorization from rights holders for text and data mining unless it is for research purposes (Engadget).
Prohibitions and Restrictions: AI systems that manipulate behavior, exploit vulnerabilities, or perform social scoring are prohibited. The act also sets out specific rules for high-risk AI applications and imposes fines for non-compliance (Passle).
For US tech firms, compliance with the EU AI Act is critical due to the EU’s significant market size
In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.
When referring to a pixel, the concept can be defined as bits per pixel (bpp).
When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.
Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.
