EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis
- URL: http://arxiv.org/abs/2410.01804v5
- Date: Tue, 29 Oct 2024 20:17:56 GMT
- Title: EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis
- Authors: Alexander Mai, Peter Hedman, George Kopanas, Dor Verbin, David Futschik, Qiangeng Xu, Falko Kuester, Jonathan T. Barron, Yinda Zhang,
- Abstract summary: We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering.
Unlike recentization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering.
We show that our method is more accurate with blending issues than 3DGS and follow-up work on view rendering.
- Score: 72.53316783628803
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recent rasterization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering, rather than alpha compositing 3D Gaussian billboards. As such, unlike 3DGS our formulation does not suffer from popping artifacts and view dependent density, but still achieves frame rates of $\sim\!30$ FPS at 720p on an NVIDIA RTX4090. Since our approach is built upon ray tracing it enables effects such as defocus blur and camera distortion (e.g. such as from fisheye cameras), which are difficult to achieve by rasterization. We show that our method is more accurate with fewer blending issues than 3DGS and follow-up work on view-consistent rendering, especially on the challenging large-scale scenes from the Zip-NeRF dataset where it achieves sharpest results among real-time techniques.
Related papers
- GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring [50.72230109855628]
We propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach.
We first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories.
By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur.
arXiv Detail & Related papers (2024-10-31T06:17:16Z) - ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings [48.72040500647568]
We present ODGS, a novelization pipeline for omnidirectional images, with geometric interpretation.
The entire pipeline is parallelized using, achieving optimization and speeds 100 times faster than NeRF-based methods.
Results show ODGS restores fine details effectively, even when reconstructing large 3D scenes.
arXiv Detail & Related papers (2024-10-28T02:45:13Z) - 3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes [50.36933474990516]
This work considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance ray tracing hardware.
To efficiently handle large numbers of semi-transparent particles, we describe a specialized algorithm which encapsulates particles with bounding meshes.
Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision.
arXiv Detail & Related papers (2024-07-09T17:59:30Z) - Bridging 3D Gaussian and Mesh for Freeview Video Rendering [57.21847030980905]
GauMesh bridges the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes.
We show that our approach adapts the appropriate type of primitives to represent the different parts of the dynamic scene.
arXiv Detail & Related papers (2024-03-18T04:01:26Z) - Deblurring 3D Gaussian Splatting [7.315329140016319]
We propose a novel real-time deblurring framework, Deblurring 3D Gaussian Splatting, using a small Multi-Layer Perceptron (MLP)
While Deblurring 3D Gaussian Splatting can still enjoy real-time rendering, it can reconstruct fine and sharp details from blurry images.
arXiv Detail & Related papers (2024-01-01T18:23:51Z) - Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering [48.41629250718956]
3D Gaussians have recently emerged as a highly efficient representation for 3D reconstruction and rendering.
Despite its high rendering quality and speed at high resolutions, they both deteriorate drastically when rendered at lower resolutions or from far away camera position.
We propose a multi-scale 3D Gaussian splatting algorithm, which maintains Gaussians at different scales to represent the same scene.
Our algorithm can achieve 13%-66% PSNR and 160%-2400% rendering speed improvement at 4$times$-128$times$ scale rendering on Mip-NeRF360 dataset.
arXiv Detail & Related papers (2023-11-28T03:31:35Z) - Compact 3D Gaussian Representation for Radiance Field [14.729871192785696]
We propose a learnable mask strategy to reduce the number of 3D Gaussian points without sacrificing performance.
We also propose a compact but effective representation of view-dependent color by employing a grid-based neural field.
Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering.
arXiv Detail & Related papers (2023-11-22T20:31:16Z) - Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering [84.37776381343662]
Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information.
We propose mip voxel grids (Mip-VoG), an explicit multiscale representation for real-time anti-aliasing rendering.
Our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously.
arXiv Detail & Related papers (2023-04-20T04:05:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.