RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis

• URL: http://arxiv.org/abs/2509.07782v1
• Date: Tue, 09 Sep 2025 14:19:19 GMT
• Title: RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis
• Authors: Hugo Blanc, Jean-Emmanuel Deschaud, Alexis Paljic,
• Abstract summary: RayGaussX builds on RayGauss by introducing key contributions that accelerate both training and inference.<n>RayGaussX achieves 5x to 12x faster training and 50x to 80x higher rendering speeds (FPS) on real-world datasets.
• Score: 3.664050435201089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: RayGauss has achieved state-of-the-art rendering quality for novel-view synthesis on synthetic and indoor scenes by representing radiance and density fields with irregularly distributed elliptical basis functions, rendered via volume ray casting using a Bounding Volume Hierarchy (BVH). However, its computational cost prevents real-time rendering on real-world scenes. Our approach, RayGaussX, builds on RayGauss by introducing key contributions that accelerate both training and inference. Specifically, we incorporate volumetric rendering acceleration strategies such as empty-space skipping and adaptive sampling, enhance ray coherence, and introduce scale regularization to reduce false-positive intersections. Additionally, we propose a new densification criterion that improves density distribution in distant regions, leading to enhanced graphical quality on larger scenes. As a result, RayGaussX achieves 5x to 12x faster training and 50x to 80x higher rendering speeds (FPS) on real-world datasets while improving visual quality by up to +0.56 dB in PSNR. Project page with videos and code: https://raygaussx.github.io/.

Related papers

• Depth-Guided Bundle Sampling for Efficient Generalizable Neural Radiance Field Reconstruction [22.057122296909142]
  High-resolution images remain computationally intensive due to the need for dense sampling of all rays.<n>We propose a novel depth-guided bundle sampling strategy to accelerate rendering.<n>Our method achieves up to a 1.27 dB PSNR improvement and a 47% increase in FPS on the DTU dataset.
  arXiv  Detail & Related papers  (2025-05-26T10:23:59Z)
• Hardware-Rasterized Ray-Based Gaussian Splatting [21.088504768655618]
  We present a novel, hardwareized rendering approach for ray-based 3D Gaussian Splatting (RayGS)<n>Our solution is the first enabling rendering RayGS models at sufficiently high frame rates to support quality-sensitive applications like Virtual and Mixed Reality.<n>Our second contribution enables alias-free rendering for RayGS, by addressing MIP-related issues during training and testing.
  arXiv  Detail & Related papers  (2025-03-24T13:53:30Z)
• BEAM: Bridging Physically-based Rendering and Gaussian Modeling for Relightable Volumetric Video [58.97416204208624]
  We present BEAM, a novel pipeline that bridges 4D Gaussian representations with physically-based rendering (PBR) to produce high-quality, relightable videos.<n>By offering realistic, lifelike visualizations under diverse lighting conditions, BEAM opens new possibilities for interactive entertainment, storytelling, and creative visualization.
  arXiv  Detail & Related papers  (2025-02-12T10:58:09Z)
• RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis [3.4341938551046227]
  Differentiable rendering methods made significant progress in novel view synthesis.<n>We provide a consistent formulation of the emitted radiance c and density sigma for differentiable ray casting of irregularly distributed Gaussians.<n>We achieve superior quality rendering compared to the state-of-the-art while maintaining reasonable training times and achieving inference speeds of 25 FPS on the Blender dataset.
  arXiv  Detail & Related papers  (2024-08-06T10:59:58Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS [47.47003067842151]
  We present RadSplat, a lightweight method for robust real-time rendering of complex scenes.<n>First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.<n>Next, we develop a novel pruning technique reducing the overall point count while maintaining high quality, leading to smaller and more compact scene representations with faster inference speeds.
  arXiv  Detail & Related papers  (2024-03-20T17:59:55Z)
• HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces [71.1071688018433]
  Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces. We improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K)
  arXiv  Detail & Related papers  (2023-12-05T22:04:49Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis.<n>We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays.<n>Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids [84.90863397388776]
  We propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without distances. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
  arXiv  Detail & Related papers  (2023-05-22T16:50:19Z)
• Spatiotemporally Consistent HDR Indoor Lighting Estimation [66.26786775252592]
  We propose a physically-motivated deep learning framework to solve the indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Our framework achieves photorealistic lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods.
  arXiv  Detail & Related papers  (2023-05-07T20:36:29Z)
• RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis [104.53930611219654]
  We present a large-scale synthetic dataset for novel view synthesis consisting of 300k images rendered from nearly 2000 complex scenes. The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis. Using 4 distinct sources of high-quality 3D meshes, the scenes of our dataset exhibit challenging variations in camera views, lighting, shape, materials, and textures.
  arXiv  Detail & Related papers  (2022-05-14T13:15:32Z)

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.