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.
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.
Point Cloud Segmentation Using PointNet
The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)
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。
PyTorch implementation of 3DQD with modifications (Deep Learning Lab - Uni Freiburg)
A Two-Phase Training Approach To Boost NeRF Reconstruction Speed
ShapeGlot: Learning Language for Shape Differentiation
Implement of PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
3D point cloud data augmentation
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.
Data Generation: Data is a spherical projection of the 3-D meshes.
CompoNET: geometric deep learning approach in architecture. From a single-image generates a building with all its components
Unsupervised Point Cloud Pose Canonicalization By Approximating the Plane/s of Symmetry
PVT: Point-Voxel Transformer for 3D Deep Learning
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
(latest updates and bug fixed) DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis
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