LinPrim: Linear Primitives for Differentiable Volumetric Rendering
Nicolas von Lützow, Matthias Nießner
摘要
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. To optimize these primitives, we present a differentiable rasterizer that runs efficiently on GPUs, allowing end-to-end gradient-based optimization while maintaining real-time 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 deepen the understanding of 3D representations by providing insights into the fidelity and performance characteristics of transparent polyhedra and suggest that adopting novel primitives can expand the available design space.
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引用它的顶会 Paper5
- MeshSplatting: Differentiable Rendering with Opaque MeshesJan Held, Sanghyun Son, Renaud Vandeghen, Daniel Rebain 等CVPR 2026 · 被引用 25 次
- Faster-GS: Analyzing and Improving Gaussian Splatting OptimizationFlorian Hahlbohm, Linus Franke, Martin Eisemann, Marcus A. MagnorCVPR 2026 · 被引用 16 次
- Radiance Meshes for Volumetric ReconstructionAlexander Mai, Trevor Hedstrom, George Kopanas, Janne Kontkanen 等CVPR 2026 · 被引用 8 次
- ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient ReconstructionSankeerth Durvasula, Sharanshangar Muhunthan, Zain Moustafa, Richard Chen 等ICCV 2025 · 被引用 6 次
- DiffBMP: Differentiable Rendering with Bitmap PrimitivesSeongmin Hong, Junghun James Kim, Daehyeop Kim, Insoo Chung 等CVPR 2026
它引用的顶会 Paper19
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman 等ICCV 2021 · 被引用 2,700 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan 等CVPR 2022 · 被引用 1,603 次
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