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
This repository contains 3D-Unet Based segmentation models with different settings. I'll be comparing different models with different settings.
list of papers, code, datasets and other resources
This is a final project for CS 674: Intelligent Visual Computing.
A point cloud generator for various 3d shapes
Group Project for 3D Spatial Learning Practical Course at TUM
awesome list of multi-view deep learning papers for 3D understanding and generation
Pytorch Implementation of Learning Local Shape Descriptors from Part Correspondences(ToG 2017, H Huang et al.): https://people.cs.umass.edu/~hbhuang/local_mvcnn/
PL-Net3D: Robust 3D Object Class Recognition Using Geometric Models
A Framework for Generalized Steady State Neural Fluid Simulations
code release for paper "Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks"(ICCV 2021)
This report contains a comprehensive study on unsupervised feature learning using various types of autoencoders.
Code and Datasets for 3D Shape Completion related publications
[AAAI-2024] Pytorch implementation of "ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field"
GRNet: Geometric Relation Network for 3D Object Detection from Point Clouds
Visualizing and understanding point cloud data, subsequently performing deep learning tasks on it.
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences - CVPRW: StruCo3D, 2023
Explore the World in 3D: 3DPointCloudLab is your gateway to the fascinating universe of 3D depth maps and point clouds. Whether you're a researcher, developer, or 3D enthusiast, our repository offers a treasure trove of tools, techniques, and insights dedicated to the exploration and manipulation of 3D spatial data.
ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes (CoRL 2022)
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