Sparse modeling of the visual world for intelligent rendering and high-performance graphics.
Solutions
Sparse Super Sampling [S3] is a revolutionary machine learning solution that delivers optimal sampling from sunlight to sensor. Our technology cuts computational complexity and delivers up to a 10x rendering speedup while preserving visual quality, which saves time, costs, energy, and carbon footprint by rendering only what matters.
Sparse Super Sampling for Games
S3 Gaming is the next leap in in-game performance, intelligently sampling only the most critical pixels to vastly accelerate rendering speed.
Sparse Super Sampling for Photo-realistic Rendering
S3 Photo revolutionizes off-line image synthesis through sparse sampling to render a minimal, yet optimal, set of key points in your complex 3D scenes.
Sparse Super Sampling for Real-Time Ray Tracing
S3 RT applies sparse sampling to accelerate the most demanding real-time ray tracing computations.
From sample placement to the number of required samples and reconstruction complexity, S3 is faster than current state-of-the-art. S3 increases frame rate and saves energy by reducing computations. This is crucial for handheld and low powered devices, and to reduce the climate footprint.
Gamer consensus is that the latency introduced by frame generation reduces the gaming experience. S3 is fast enough not to need frame generation. S3 can be integrated as a light layer on top of any rendering engine (no matter real-time or offline).
Our optimal sampling is guaranteed to achieve a user-defined quality, no matter the scene complexity. S3 optimizes rendering speed with zero compromises in visual quality. Achieving a target visual quality is mathematically and empirically guaranteed.
The S3 sparse visual world model relies on few-shot training and does not need a data center to train. In fact, a few minutes on a consumer laptop suffices to train and fine-tune the model.
The sparse world model generalizes from single scenes and game levels to full games and entire game portfolios, requiring only a few training images never leaving your premises. From this perspective, S3 democratizes upscaling for gamers and game developers.
Sparsit is a research oriented company at the intersection between computer graphics, vision, and machine learning.
Our mission is to develop a sparse visual model of the world that enables extremely efficient and accurate measurement, synthesis, and analysis through sparse processing.
Sparse rendering is a next generation rendering algorithm for photo-realistic image synthesis and real-time rendering, that samples a small set of key points in a 3D scene and produces output images or image streams at up to 10x the speed without any compromises in quality.
Sparsit rendering suite includes image and video synthesis and analysis with applications and plugins at different stages in the rendering pipelines for both off-line (CPU) rendering such as product, architectural visualization, and VFX, as well as real-time (GPU) rendering for video-games, and interactive applications.
Our visual world model describes the world around us using a small set of sparse features that are learned according to a user-defined quality target.
Optimal sampling
Based on the sparse visual world model, we derive optimal sampling patterns and measurement strategies that can be used in a wide range of applications including computer graphics rendering and visualization, single- and multi-sensor image and video capture, radiance fields, or LiDAR simulation and measurement to name a few.
Sparse rendering
The goal of sparse rendering is to reduce the computational complexity in photo-realistic image synthesis for off-line and real-time applications.
Not all pixels are created equal: Based on the sparse visual model, sparse rendering intelligently predicts which pixels in the image contribute the most to the final picture and renders only those pixels, leading to a significant reduction in rendering time.
Up to10x
The computational savings (time and energy footprint) scales with the number of pixels in the sampling patterns, e.g, an average 10% sampling ratio leads to a 10x speedup in rendering.
Sparse rendering relies on machine learning to find the optimal number of pixel samples and pixel sampling patterns depending on the user-defined target quality setting (low, medium, or high).
Each pixel within the optimal sampling patterns is rendered as per usual by the renderer, and the reconstruction algorithm finalizes the image based on the sparse samples. The reconstruction quality always follows the target quality, no matter how complex the scene is.