How to Explore Real-Time Rendering Innovations

This is pure joy. What you see is not any video created by AI, but a 3D scene constructed using images by AI. In 3D scene representations, mesh & point-based systems are utilized due to their explicit nature & compatibility with rapid GPU/CUDA-based rasterization. In contrast, recent techniques in Neural Radiance Field (NeRF) have leveraged continuous scene representations. These methods typically employ optimization of a Multi-Layer Perceptron (MLP) using volumetric ray-marching for the synthesis of new views of captured scenes. Efficient radiance field solutions also exploit continuous representations, interpolating values in various data structures like voxel and hash grids, points. Although the continuous nature of these methods supports optimization, the random sampling needed for rendering can be expensive & may cause noise. A new approach has been developed that merges advantages of both continuous & explicit representations. The 3D Gaussian representation enhances optimization with top-tier visual quality & competitive training times. The tile-based splatting solution guarantees real-time rendering at high quality for standard resolution across several known datasets. The aim is to enable real-time rendering of scenes captured with multiple photographs & to generate representations with optimization speeds matching or even exceeding the most efficient prior methods for regular real scenes. While some methods achieve quick training, they often struggle to reach the visual quality attained by the top-tier NeRF methods that may necessitate extended training times. Faster radiance field methods can achieve interactive rendering speeds but may not achieve real-time rendering at higher resolutions. The proposed work has 3 parts: 3D Gaussians: Introduced as a versatile scene representation, 3D Gaussians allow for high-quality results with inputs like those used in prior NeRF-like techniques. Optimization of 3D Gaussian Properties: This involves the adjustment of the 3D position, opacity, anisotropic covariance & spherical harmonic coefficients, coupled with adaptive density control, creating a precise scene representation. Real-Time Rendering Solution: Inspired by tile-based rasterization, this solution utilizes fast GPU sorting algorithms & anisotropic splatting to ensure accurate rendering. Main contributions are: The utilization of anisotropic 3D Gaussians as unstructured representation of radiance fields. A method for optimizing 3D Gaussian properties to create top representations for captured scenes. A GPU-compatible, visibility-aware rendering technique allowing anisotropic splatting & quick backpropagation for novel view synthesis. This method can optimize 3D Gaussians from multi-view captures & attain quality surpassing the best previous implicit radiance field approaches. It can also achieve training speeds comparable to the fastest methods, offering the first real-time rendering with high quality for novel-view synthesis.

#GaussianSplattering is similar to a #NeRF in that it can recreate a scene in 3D from a few images. However, the GS technique is not only exponentially faster, it has little to no artifacts that require cleanup ("NeRF Floaters"), and can run in real-time at 1080p. Abstract: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 100 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets. _ #synthography #vfx

Here's my 2024 LinkedIn Rewind, by Coauthor: 2024 proved that 3D Gaussian splatting isn't just another tech trend - it's transforming how we capture and understand the world around us. From real-time architectural visualization to autonomous vehicle training, we're seeing practical implementations I could only dream about a year ago. Through my "100 Days of Splats" project, I witnessed this technology evolve from research papers to real-world applications. We saw: → Large-scale scene reconstruction becoming practical → Real-time rendering reaching 60+ FPS → Integration with game engines and VFX pipelines → Adoption by major companies like Meta, Nvidia, and Varjo Three posts that captured pivotal developments: "VastGaussians - First Method for High-Quality Large Scene Reconstruction" Finally bridging the gap between research and AEC industry needs "This research is specifically tailored for visualization of large scenes such as commercial and industrial buildings, quarries, and landscapes." https://lnkd.in/gvgpqMNe "2D Gaussian Splatting vs Photogrammetry" The first radiance fields project producing truly accurate geometry "All in one pipeline I can generate a radiance field, textured mesh, and fly renderings - all in less than an hour" https://lnkd.in/geprBw6j "HybridNeRF Development" Pushing rendering speeds while maintaining quality "HybridNeRF looks better than 3DGS and can achieve over 60 FPS framerate" https://lnkd.in/gcqdE4iD Speaking at Geo Week showed me how hungry the industry is for practical applications of these technologies. We're no longer asking if Gaussian splatting will be useful - we're discovering new uses every day. 2025 will be about scaling practical applications - from AEC to geospatial to virtual production. The foundation is laid; now it's time to build. To everyone exploring and pushing the boundaries of 3D visualization - your experiments today are tomorrow's innovations. Keep building, keep sharing, keep pushing what's possible. #ComputerVision #3D #AI #GuassianSplatting #LinkedInRewind