LinPrim: Linear Primitives for Differentiable Volumetric Rendering

• URL: http://arxiv.org/abs/2501.16312v2
• Date: Tue, 28 Jan 2025 12:52:41 GMT
• Title: LinPrim: Linear Primitives for Differentiable Volumetric Rendering
• Authors: Nicolas von Lützow, Matthias Nießner,
• Abstract summary: We introduce two new scene representations based on linear primitives-octahedra and tetrahedra-both of which define homogeneous volumes bounded by triangular faces. This formulation aligns naturally with standard mesh-based tools, minimizing overhead for downstream applications. We demonstrate comparable performance to state-of-the-art volumetric methods while requiring fewer primitives to achieve similar reconstruction fidelity.
• Score: 53.780682194322225
• License:
• Abstract: Volumetric rendering has become central to modern novel view synthesis methods, which use differentiable rendering to optimize 3D scene representations directly from observed views. While many recent works build on NeRF or 3D Gaussians, we explore an alternative volumetric scene representation. More specifically, we introduce two new scene representations based on linear primitives-octahedra and tetrahedra-both of which define homogeneous volumes bounded by triangular faces. This formulation aligns naturally with standard mesh-based tools, minimizing overhead for downstream applications. To optimize these primitives, we present a differentiable rasterizer that runs efficiently on GPUs, allowing end-to-end gradient-based optimization while maintaining realtime rendering capabilities. Through experiments on real-world datasets, we demonstrate comparable performance to state-of-the-art volumetric methods while requiring fewer primitives to achieve similar reconstruction fidelity. Our findings provide insights into the geometry of volumetric rendering and suggest that adopting explicit polyhedra can expand the design space of scene representations.

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)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• 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)
• Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives [70.32817882783608]
  We present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. Unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points.
  arXiv  Detail & Related papers  (2023-07-11T17:58:31Z)
• NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support [43.5015470997138]
  We present a method for generating high-quality watertight manifold meshes from multi-view input images. Our method combines the benefits of both worlds; we take the geometry obtained from neural fields, and further optimize the geometry as well as a compact neural texture representation.
  arXiv  Detail & Related papers  (2023-05-26T17:59:21Z)
• 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)
• Multi-View Mesh Reconstruction with Neural Deferred Shading [0.8514420632209809]
  State-of-the-art methods use both neural surface representations and neural shading. We represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle rendering and neural shading. We evaluate our runtime on a public 3D reconstruction dataset and show that it can match the reconstruction accuracy of traditional baselines while surpassing them in optimization.
  arXiv  Detail & Related papers  (2022-12-08T16:29:46Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)

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.