PyTorch implementation of 3DQD with modifications (Deep Learning Lab - Uni Freiburg)
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Updated
Nov 10, 2023 - Python
PyTorch implementation of 3DQD with modifications (Deep Learning Lab - Uni Freiburg)
A Two-Phase Training Approach To Boost NeRF Reconstruction Speed
Implement of PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
Data Generation: Data is a spherical projection of the 3-D meshes.
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。
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
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysis
Official GitHub repo for VecKM. A very efficient and descriptive local geometry encoder / point tokenizer / patch embedder. ICML2024.
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.
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
Learning Graph-Convolutional Representations for Point Cloud Denoising (ECCV 2020)
Graph-convolutional GAN for point cloud generation. Code from ICLR 2019 paper Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Official implementation of GraphX-Convolution
Rendering color and depth images for ShapeNet models.
PVT: Point-Voxel Transformer for 3D Deep Learning
code for "Neural Cages for Detail-Preserving 3D Deformations"
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.
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