Rendering Anywhere You See: Renderability Field-guided Gaussian Splatting

• URL: http://arxiv.org/abs/2504.19261v1
• Date: Sun, 27 Apr 2025 14:41:01 GMT
• Title: Rendering Anywhere You See: Renderability Field-guided Gaussian Splatting
• Authors: Xiaofeng Jin, Yan Fang, Matteo Frosi, Jianfei Ge, Jiangjian Xiao, Matteo Matteucci,
• Abstract summary: We propose renderability field-guided gaussian splatting (RF-GS) for scene view synthesis.<n>RF-GS quantifies input inhomogeneity through a renderability field, guiding pseudo-view sampling to enhanced visual consistency.<n>Our experiments on simulated and real-world data show that our method outperforms existing approaches in rendering stability.
• Score: 4.89907242398523
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scene view synthesis, which generates novel views from limited perspectives, is increasingly vital for applications like virtual reality, augmented reality, and robotics. Unlike object-based tasks, such as generating 360{\deg} views of a car, scene view synthesis handles entire environments where non-uniform observations pose unique challenges for stable rendering quality. To address this issue, we propose a novel approach: renderability field-guided gaussian splatting (RF-GS). This method quantifies input inhomogeneity through a renderability field, guiding pseudo-view sampling to enhanced visual consistency. To ensure the quality of wide-baseline pseudo-views, we train an image restoration model to map point projections to visible-light styles. Additionally, our validated hybrid data optimization strategy effectively fuses information of pseudo-view angles and source view textures. Comparative experiments on simulated and real-world data show that our method outperforms existing approaches in rendering stability.

Related papers

• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• Stable Virtual Camera: Generative View Synthesis with Diffusion Models [51.71244310522393]
  We present Stable Virtual Camera (Seva), a generalist diffusion model that creates novel views of a scene.<n>Our approach overcomes these limitations through simple model design, optimized training recipe, and flexible sampling strategy.<n>Our method can generate high-quality videos lasting up to half a minute with seamless loop closure.
  arXiv  Detail & Related papers  (2025-03-18T17:57:22Z)
• AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Prediction [69.65671384868344]
  We propose AR-1-to-3, a novel next-view prediction paradigm based on diffusion models.<n>We show that our method significantly improves the consistency between the generated views and the input views, producing high-fidelity 3D assets.
  arXiv  Detail & Related papers  (2025-03-17T08:39:10Z)
• Synthesizing Consistent Novel Views via 3D Epipolar Attention without Re-Training [102.82553402539139]
  Large diffusion models demonstrate remarkable zero-shot capabilities in novel view synthesis from a single image.<n>These models often face challenges in maintaining consistency across novel and reference views.<n>We propose to use epipolar geometry to locate and retrieve overlapping information from the input view.<n>This information is then incorporated into the generation of target views, eliminating the need for training or fine-tuning.
  arXiv  Detail & Related papers  (2025-02-25T14:04:22Z)
• SimVS: Simulating World Inconsistencies for Robust View Synthesis [102.83898965828621]
  We present an approach for leveraging generative video models to simulate the inconsistencies in the world that can occur during capture. We demonstrate that our world-simulation strategy significantly outperforms traditional augmentation methods in handling real-world scene variations.
  arXiv  Detail & Related papers  (2024-12-10T17:35:12Z)
• 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)
• Sampling for View Synthesis: From Local Light Field Fusion to Neural Radiance Fields and Beyond [27.339452004523082]
  Local light field fusion proposes an algorithm for practical view synthesis from an irregular grid of sampled views. We achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We reprise some of the recent results on sparse and even single image view synthesis.
  arXiv  Detail & Related papers  (2024-08-08T16:56:03Z)
• Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering [16.382098950820822]
  We propose Zero-to-Hero, a novel test-time approach that enhances view synthesis by manipulating attention maps. We modify the self-attention mechanism to integrate information from the source view, reducing shape distortions. Results demonstrate substantial improvements in fidelity and consistency, validated on a diverse set of out-of-distribution objects.
  arXiv  Detail & Related papers  (2024-05-29T00:58:22Z)
• Cross-view Self-localization from Synthesized Scene-graphs [1.9580473532948401]
  Cross-view self-localization is a challenging scenario of visual place recognition in which database images are provided from sparse viewpoints. We propose a new hybrid scene model that combines the advantages of view-invariant appearance features computed from raw images and view-dependent spatial-semantic features computed from synthesized images.
  arXiv  Detail & Related papers  (2023-10-24T04:16:27Z)
• Learning to Render Novel Views from Wide-Baseline Stereo Pairs [26.528667940013598]
  We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. Existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry. We propose an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray.
  arXiv  Detail & Related papers  (2023-04-17T17:40:52Z)
• Stable View Synthesis [100.86844680362196]
  We present Stable View Synthesis (SVS) Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. SVS outperforms state-of-the-art view synthesis methods both quantitatively and qualitatively on three diverse real-world datasets.
  arXiv  Detail & Related papers  (2020-11-14T07:24:43Z)

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.