Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration

• URL: http://arxiv.org/abs/2403.11056v2
• Date: Wed, 3 Apr 2024 04:00:53 GMT
• Title: Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
• Authors: Zhihao Liang, Qi Zhang, Wenbo Hu, Ying Feng, Lei Zhu, Kui Jia,
• Abstract summary: 3DGS is not alias-free, and its rendering at varying resolutions could produce severe blurring or jaggies. This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels. We introduce this approximation in the two-dimensional pixel shading, and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area.
• Score: 49.004898985671815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 3D Gaussian Splatting (3DGS) gained its popularity recently by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free, and its rendering at varying resolutions could produce severe blurring or jaggies. This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels. Consequently, this discrete sampling scheme inevitably results in aliasing, owing to the restricted sampling bandwidth. In this paper, we derive an analytical solution to address this issue. More specifically, we use a conditioned logistic function as the analytic approximation of the cumulative distribution function (CDF) in a one-dimensional Gaussian signal and calculate the Gaussian integral by subtracting the CDFs. We then introduce this approximation in the two-dimensional pixel shading, and present Analytic-Splatting, which analytically approximates the Gaussian integral within the 2D-pixel window area to better capture the intensity response of each pixel. Moreover, we use the approximated response of the pixel window integral area to participate in the transmittance calculation of volume rendering, making Analytic-Splatting sensitive to the changes in pixel footprint at different resolutions. Experiments on various datasets validate that our approach has better anti-aliasing capability that gives more details and better fidelity.

Related papers

• GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views [67.34073368933814]
  We propose a generalizable Gaussian Splatting approach for high-resolution image rendering under a sparse-view camera setting. We train our Gaussian parameter regression module on human-only data or human-scene data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. Experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
  arXiv  Detail & Related papers  (2024-11-18T08:18:44Z)
• Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering [81.88246351984908]
  We propose a unified optimization method to make Gaussians adaptive for arbitrary scales. Inspired by the mipmap technique, we design pseudo ground-truth for the target scale and propose a scale-consistency guidance loss to inject scale information into 3D Gaussians. Our method outperforms 3DGS in PSNR by an average of 9.25 dB for zoom-in and 10.40 dB for zoom-out.
  arXiv  Detail & Related papers  (2024-08-12T16:49:22Z)
• MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification [8.099621725105857]
  We introduce MVG-Splatting, a solution guided by Multi-View considerations. We propose an adaptive quantile-based method that dynamically determines the level of additional densification. This approach significantly enhances the overall fidelity and accuracy of the 3D reconstruction process.
  arXiv  Detail & Related papers  (2024-07-16T15:24:01Z)
• End-to-End Rate-Distortion Optimized 3D Gaussian Representation [33.20840558425759]
  We formulate the compact 3D Gaussian learning as an end-to-end Rate-Distortion Optimization problem. We introduce dynamic pruning and entropy-constrained vector quantization (ECVQ) that optimize the rate and distortion at the same time. We verify our method on both real and synthetic scenes, showcasing that RDO-Gaussian greatly reduces the size of 3D Gaussian over 40x.
  arXiv  Detail & Related papers  (2024-04-09T14:37:54Z)
• pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction [26.72289913260324]
  pixelSplat is a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time.
  arXiv  Detail & Related papers  (2023-12-19T17:03:50Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)
• Mip-Splatting: Alias-free 3D Gaussian Splatting [52.366815964832426]
  3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. Strong artifacts can be observed when changing the sampling rate, eg, by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter.
  arXiv  Detail & Related papers  (2023-11-27T13:03:09Z)
• Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild [96.09941542587865]
  We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In this way, we precisely align 3D models to objects in RGB images which results in significantly improved 3D pose estimates. We evaluate our approach on the challenging Pix3D dataset and achieve up to 55% relative improvement compared to state-of-the-art refinement methods in multiple metrics.
  arXiv  Detail & Related papers  (2020-07-17T12:34:38Z)

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.