PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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Updated
Jan 23, 2018 - Python
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
CSGNet for voxel based input
3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.
This report contains a comprehensive study on unsupervised feature learning using various types of autoencoders.
KITTI data processing and 3D CNN for Vehicle Detection
Fuse multiple depth frames into a TSDF voxel volume.
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
Pytorch Implementation of Learning Local Shape Descriptors from Part Correspondences(ToG 2017, H Huang et al.): https://people.cs.umass.edu/~hbhuang/local_mvcnn/
A point cloud generator for various 3d shapes
Paper list of deep learning on point clouds.
PointRCNN configured to Argoverse/Custom dataset
3D Shape Generation Baselines in PyTorch.
PL-Net3D: Robust 3D Object Class Recognition Using Geometric Models
Visualizing and understanding point cloud data, subsequently performing deep learning tasks on it.
Code base of ParSeNet: ECCV 2020.
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020)
This repository contains the source codes for the paper "Unsupervised cycle-consistent deformation for shape matching".
Group Project for 3D Spatial Learning Practical Course at TUM
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