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NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support

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arxiv 2305.17134 v3 pith:KGDDRVMH submitted 2023-05-26 cs.CV

Xinyue Wei , Fanbo Xiang , Sai Bi , Anpei Chen , Kalyan Sunkavalli , Zexiang Xu , Hao Su This is my paper · ORCID
classification cs.CV
keywords neuralrenderinggeneratehigh-qualitymeshesmethodmethodsdifferentiable
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We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend to generate noisy meshes with poor topology. Differentiable rasterization-based methods can generate high-quality meshes but are sensitive to initialization. Our method combines the benefits of both worlds; we take the geometry initialization obtained from neural volumetric fields, and further optimize the geometry as well as a compact neural texture representation with differentiable rasterizers. Through extensive experiments, we demonstrate that our method can generate accurate mesh reconstructions with faithful appearance that are comparable to previous volume rendering methods while being an order of magnitude faster in rendering. We also show that our generated mesh and neural texture reconstruction is compatible with existing graphics pipelines and enables downstream 3D applications such as simulation. Project page: https://sarahweiii.github.io/neumanifold/

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DMesh++: An Efficient Differentiable Mesh for Complex Shapes

    cs.CV 2024-12 conditional novelty 6.0 of 10

    DMesh++ speeds up differentiable mesh generation by using a Minimum-Ball condition instead of weighted Delaunay triangulation, enabling faster reconstruction of complex shapes.

  2. Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale

    cs.GR 2024-12 conditional novelty 6.0 of 10

    Meshtron autoregressively generates 3D meshes with up to 64K faces at 1024-level coordinate resolution, a large scale increase over prior work, using an hourglass transformer and sliding-window inference.

  3. Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views

    cs.CV 2024-12 conditional novelty 5.0 of 10

    Pragmatist turns sparse unposed photos of an object into a high-fidelity 3D mesh by generating consistent canonical views with a diffusion model, reconstructing a triplane mesh, then refining camera poses and texture ...

  4. ARM: Appearance Reconstruction Model for Relightable 3D Generation

    cs.CV 2024-11 conditional novelty 5.0 of 10

    ARM is a feed-forward model that reconstructs a 3D mesh and PBR texture maps (albedo, roughness, metalness) from sparse-view images, improving texture sharpness and relighting quality over prior single-image-to-3D methods.

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