LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation

Created by Zeyu HU

Introduction

This work is based on our paper LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation, which appears at the European Conference on Computer Vision (ECCV) 2022.

We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained model should generate robust results irrespective of viewpoints for scene scanning and thus the inconsistencies in model predictions across frames provide a very reliable measure of uncertainty for active sample selection. To implement this uncertainty measure, we introduce new inter-frame divergence and entropy formulations, which serve as the metrics for active selection. Moreover, we demonstrate additional performance gains by predicting and incorporating pseudo-labels, which are also selected using the proposed inter-frame uncertainty measure. Experimental results validate the effectiveness of LiDAL: we achieve 95% of the performance of fully supervised learning with less than 5% of annotations on the SemanticKITTI and nuScenes datasets, outperforming state-of-the-art active learning methods.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{hu2022lidal,
title={LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation},
author={Hu, Zeyu and Bai, Xuyang and Zhang, Runze and Wang, Xin and Sun, Guangyuan and Fu, Hongbo and Tai, Chiew-Lan},
booktitle={European Conference on Computer Vision},
pages={248--265},
year={2022},
organization={Springer}
}

Installation

• For linux-64 platform with conda, the enviroment can be created by requirements.txt:

  conda create --name <env> --file <this file>
  

• Our code is based on Pytorch. Please make sure CUDA and cuDNN are installed. One configuration has been tested:

  • Python 3.9.6
  • Pytorch 1.9.0
  • torchvision 0.10.0
  • CUDA 10.2
  • cudatoolkit 10.2.89
  • cuDNN 7.6.5

• VMNet depends on the torchsparse library. Please follow its installation instructions. One configuration has been tested:

  • torchsparse 1.4.0

Data Preparation

• Please refer to http://www.semantic-kitti.org/ and https://www.nuscenes.org/ to get access to the SemanticKITTI and nuScenes dataset. This code is based on their official file orginizations. Please put the files in directories 'Semantic_kitti/' and 'nuScenes/' accordingly.

• Prepare sub-scene regions. k-means-constrained

  python -m dataset.prepare_supervoxel_kmeans_sk
  python -m dataset.prepare_supervoxel_kmeans_nu
  

• Prepare kdtree.

  python -m dataset.prepare_kdtree_sk
  python -m dataset.prepare_kdtree_nu
  

• ReDAL

  To implement the ReDAL method in this code, the sub-scene regions are divided by the VCCS algorithm.

  • Dependency

    • Point CLoud Library (PCL)
    • Boost C++ Library
    • CMake
    • cnpy

  • Build the project in pcl_related/ via CMake

  Then run:

  python -m dataset.prepare_supervoxel_VCCS_sk
  python -m dataset.prepare_supervoxel_VCCS_nu  
  

  Also, it requires the calculation of surface variation.

  python -m dataset.ReDAL.gen_surface_variation_sk
  python -m dataset.ReDAL.gen_surface_variation_nu
  

Run

• Fully supervised learning.

  CUDA_VISIBLE_DEVICES=X python train.py --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --label_unit fr --metric_name full --r_id -1
  

• Zero round initialization.

  CUDA_VISIBLE_DEVICES=X python train.py --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --label_unit fr --metric_name None --r_id 0
  

  • For bechmarking purposes, the initial models used in our experiments can be found at:
    • SK_SPVCNN_0r
    • SK_Mink_0r
    • NU_SPVCNN_0r
    • NU_Mink_0r

• K round active learning.

  • Supported active method flags:
    • Frame-based
      • Random Selection (RAND)
      • Segment-entropy (SEGENT)
      • Softmax Margin (MAR)
      • Softmax Confidence (CONF)
      • Softmax Entropy (ENT)
      • Core-set Selection (CSET)
    • Region-based
      • Random Selection (RAND)
      • ReDAL
      • LiDAL (ours)
  1. Probability inference of current trained model (K - 1 round).

  CUDA_VISIBLE_DEVICES=X python -m score.prob_inference --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --label_unit [fr, sv] --metric_name [...] --r_id (K-1)
  

  2. Uncertainty scoring and sample selection. Examples below:

  python -m score.frame_level.core_set --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --r_id K
  python -m score.sv_level.LiDAL --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --r_id K
  

  3. Training of K round.

  CUDA_VISIBLE_DEVICES=X python train.py --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --label_unit [fr, sv] --metric_name [...] --r_id K
  

  4. Validation of K round.

  CUDA_VISIBLE_DEVICES=X python evaluate.py --dataset_name [SK, NU] --model_name [SPVCNN, Mink] --label_unit [fr, sv] --metric_name [...] --r_id K