3D AffordanceNet

This repository is the official experiment implementation of 3D AffordanceNet benchmark.

3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

This repository implements two baseline methods: PointNet++ and DGCNN on four proposed affordance understanding tasks: Full-Shape, Partial-View, Rotation-Invariant, Semi-Supervised Affordance Estimation.

You can reproduce the performances described in the origin paper by simply running a command down below.

[CVPR 2021 Paper] [Dataset Download Link] [Project Page]

Requirements

All the codes are tested in the following environment:

• Linux (tested on Ubuntu 16.04)
• Python 3.7+
• PyTorch 1.0.1
• Gorilla-Core
• CUDA 10.0 or higher

You can install the required packages by running the following command:

pip install -r requirement.txt

To install the cuda kernel, go to models/pointnet2_ops and run the following command:

python setup.py build_ext --inplace

Quick Start

The following set up is for DGCNN, you can change to PointNet++ accordingly.

First download the whole dataset from here and extract the files to the data_root, then modify the dataset data_root in configuration(full-shape for example), the dataset data_root should obey the data structure below:

data_root
    ├── task_train_data.pkl
    ├── task_val_data.pkl
    └── task_test_data.pkl

Then to train a model from scratch:

python train.py config/dgcnn/estimation_cfg.py --work_dir TPATH_TO_LOG_DIR --gpu 0,1

After training, to test a model:

python test.py config/dgcnn/estimation_cfg.py --work_dir PATH_TO_LOG_DIR --gpu 0,1 --checkpoint PATH_TO_CHECKPOINT

Currently Support

• Models
  • DGCNN
  • PointNet++
• Tasks
  • Full-Shape Affordance Estimation
  • Partial-View Affordance Estimation
  • Rotation-Invariant Affordance Estimation
  • Semi-Supervised Affordance Estimation