PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
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Updated
Apr 24, 2024 - Python
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation. In ICCV2019.
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
code for "Neural Cages for Detail-Preserving 3D Deformations"
PVT: Point-Voxel Transformer for 3D Deep Learning
Rendering color and depth images for ShapeNet models.
Official implementation of GraphX-Convolution
Graph-convolutional GAN for point cloud generation. Code from ICLR 2019 paper Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Learning Graph-Convolutional Representations for Point Cloud Denoising (ECCV 2020)
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis
A new method to preprocess ShapeNet to get minimal shift 3D ground truth; 3 Stage single-view 3D reconstruction method; Point cloud surface reconstruction without input normals.
Unsupervised Point Cloud Pose Canonicalization By Approximating the Plane/s of Symmetry
Python module to read and write .binvox files, Contributions come from dimatura/binvox-rw-py. Fixed some bugs and packaged them into installable Python packages。
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