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.

Overview

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]

GroundTruth

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
Owner
Research lab focusing on CV, ML, and AI
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