Nonwatertight Mesh Reconstruction
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Abstract (arXiv)
Reconstructing 3D non-watertight mesh from an unoriented point cloud is an unexplored area in computer vision and computer graphics. In this project, we tried to tackle this problem by extending the learning-based watertight mesh reconstruction pipeline presented in the paper 'Shape as Points'. The core of our approach is to cast the problem as a semantic segmentation problem that identifies the region in the 3D volume where the mesh surface lies and extracts the surfaces from the detected regions. Our approach achieves compelling results compared to the baseline techniques.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 26 Jun 2022.
Builds on
7 of 11 references.
References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2020Convolutional Occupancy Networkscited by 761
- 2021Shape As Points: A Differentiable Poisson Solvercited by 187
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