Ghost on the Shell: An Expressive Representation of General 3D Shapes
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Abstract (arXiv)
The creation of photorealistic virtual worlds requires the accurate modeling of 3D surface geometry for a wide range of objects. For this, meshes are appealing since they 1) enable fast physics-based rendering with realistic material and lighting, 2) support physical simulation, and 3) are memory-efficient for modern graphics pipelines. Recent work on reconstructing and statistically modeling 3D shape, however, has critiqued meshes as being topologically inflexible. To capture a wide range of object shapes, any 3D representation must be able to model solid, watertight, shapes as well as thin, open, surfaces. Recent work has focused on the former, and methods for reconstructing open surfaces do not support fast reconstruction with material and lighting or unconditional generative modelling. Inspired by the observation that open surfaces can be seen as islands floating on watertight surfaces, we parameterize open surfaces by defining a manifold signed distance field on watertight templates. With this parameterization, we further develop a grid-based and differentiable representation that parameterizes both watertight and non-watertight meshes of arbitrary topology. Our new representation, called Ghost-on-the-Shell (G-Shell), enables two important applications: differentiable rasterization-based reconstruction from multiview images and generative modelling of non-watertight meshes. We empirically demonstrate that G-Shell achieves state-of-the-art performance on non-watertight mesh reconstruction and generation tasks, while also performing effectively for watertight meshes.
Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 50 of its 74 references that are in the map. First posted on arXiv 23 Oct 2023.
Builds on
50 of 74 references.
References · 50
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- 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 553
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- 2020Modular Primitives for High-Performance Differentiable Renderingcited by 441
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- 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
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- 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 199
- 2021Shape As Points: A Differentiable Poisson Solvercited by 187
- 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 180
- 2022Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoisingcited by 163
- 2019SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimizationcited by 159
- 2023Flexible Isosurface Extraction for Gradient-Based Mesh Optimizationcited by 139
- 2019CLOTH3D: Clothed 3D Humanscited by 116
- 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
- 2021NeRF-VAE: A Geometry Aware 3D Scene Generative Modelcited by 112
- 2022Highly Accurate Dichotomous Image Segmentationcited by 110
- 2019Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networkscited by 106
- 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
- 2020Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Imagescited by 103
- 2022SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generationcited by 92
- 2021MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networkscited by 76
- 2022Neural Dual Contouringcited by 71
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- 2023NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Imagescited by 33
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- 2023NSF: Neural Surface Fields for Human Modeling from Monocular Depthcited by 21
- 2021Deep Hybrid Self-Prior for Full 3D Mesh Generationcited by 13
- 2022Stochastic Poisson Surface Reconstructioncited by 13
- 2021DeepCurrents: Learning Implicit Representations of Shapes with Boundariescited by 12
- 2022Structural Causal 3D Reconstructioncited by 12
- 2023Learning a Diffusion Prior for NeRFscited by 9
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Citing papers per year: 2023: 1; 2024: 14; 2025: 6; 2026: 3.
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