This repository provides the official PyTorch implementation for the following paper:

Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
Kevin Tirta Wijaya, Dong-Hee Paek, and Seung-Hyun Kong
[arXiv]

Abstract: Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, such a process may result in a substantial loss of granular information due to the sampling operation. Moreover, the widely-used max-pooling feature aggregation may exacerbate the loss since it completely neglects information from non-maximum point features. Due to the compounded loss of information concerning granularity and non-maximum point features, the resulting high-semantic point features from existing networks could be insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes. To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP). The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information. On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features. Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features. Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud. In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point clouds. Specifically, PointStack outperforms various existing feature learning networks for shape classification and part segmentation on the ScanObjectNN and ShapeNetPart datasets.

To install the requirements:

# 1. Create new environment
conda create -n <environment name> python=3.7

# 2. Install PyTorch with corresponding cudatoolkit, for example,
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

# 3. Install other requirements
pip install -r requirements.txt

To prepare the dataset:

1. Create empty directories for 'modelnet40', 'partnormal', and 'scanobjectnn' inside './data' directory.
2. Download the corresponding dataset for Modelnet40, ShapeNetPart, and ScanObjectNN.
3. Create 'data' and 'experiments' directories on the workspace, and organize as follows,

./
  |-- cfgs
  |-- core
  |-- data
        |-- modelnet40
              |-- .json files
              |-- .txt files
        |-- partnormal
              |-- dirs
              |-- synsetoffset2category.txt
        |-- scanobjectnn
              |-- dirs
  |-- experiments

To train the network, run this command:

python train.py --cfg_file <path_to_cfg_yaml (str)> --exp_name <experiment_name (str)> --val_steps <eval_every_x_epoch (int)>

To evaluate the network with pre-trained weights, run:

python test.py --cfg_file <path_to_cfg_yaml (str)> --ckpt <path_to_ckpt_pth (str)>

Our pretrained model achieves the following performances on :