🔥 Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation [CVPR 2023]
Li Li, Hubert P. H. Shum and Toby P. Breckon, In Proc. International Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2023 [homepage] [pdf] [video] [poster]
cvpr2023_777_video.mp4
Abstract: Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3× reduction in model parameters and 641× fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).
[2024/01/30] We release the ST-RFD training split on SemanticKITTI dataset.
[2023/06/21] 🇨🇦 We will present our work in West Building Exhibit Halls ABC 108 @ Wed 21 Jun 10:30 a.m. PDT — noon PDT. See you in Vancouver, Canada.
[2023/06/20] Code released.
[2023/02/27] LiM3D was accepted at CVPR 2023!
The data is organized in the format of {SemanticKITTI} U {ScribbleKITTI}.
sequences/
├── 00/
│ ├── scribbles/
│ │ ├ 000000.label
│ │ ├ 000001.label
│ │ └ .......label
│ ├── labels/
│ ├── velodyne/
│ ├── image_2/
│ ├── image_3/
│ ├── times.txt
│ ├── calib.txt
│ └── poses.txt
├── 01/
├── 02/
.
.
└── 21/
Please follow the instructions from SemanticKITTI to download the dataset including the KITTI Odometry point cloud data.
Please download ScribbleKITTI scribble annotations and unzip in the same directory. Each sequence in the train-set (00-07, 09-10) should contain the velodyne, labels and scribbles directories.
Move the sequences folder or make a symbolic link to a new directory inside the project dir called data/. Alternatively, edit the dataset: root_dir field of each config file to point to the sequences folder.
For the installation, we recommend setting up a virtual environment using conda or venv:
For conda,
conda env create -f environment.yaml
conda activate lim3d
pip install -r requirements.txtFor venv,
python -m venv ~/venv/lim3d
source ~/venv/scribblekitti/bin/activate
pip install -r requirements.txtFurthermore install the following dependencies:
- pytorch (tested with version
1.10.1+cu111) - pytorch-lightning (tested with version
1.6.5) - torch-scatter (tested with version
2.0.9) - spconv (tested with version
2.1.21)
Our overall architecture involves three stages (Figure 2). You can reproduce our results through the scripts provided in the experiments folder:
- Training: we utilize reflectivity-prior descriptors and adapt the Mean Teacher framework to generate high-quality pseudo-labels. Running with bash script:
bash experiments/train.sh; - Pseudo-labeling: we fix the trained teacher model prediction in a class-range-balanced manner, expanding dataset with Reflectivity-based Test Time Augmentation (Reflec-TTA) during test time. Running with bash script:
bash experiments/crb.sh, then save the pseudo-labelsbash experiments/save.sh; - Distillation with unreliable predictions: we train on the generated pseudo-labels, and utilize unreliable pseudo-labels in a category-wise memory bank for improved discrimination. Running with bash script:
bash experiments/dist-reflec.sh.
Please refer to our supplementary video and supplementary documentation for more qualitative results.
You can download our pretrained models here via Onedrive.
To validate the results, please refer to the scripts in experiments folder, and put the pretrained models in the models folder. Specify CKPT_PATH and SAVE_DIR in predict.sh file.
For example, if you want to validate the results of 10% labeled training frames + LiM3D (without SDSC) + with reflectivity features on ScribbleKITTI, you can specify CKPT_PATH as model/sck_crb10_feat69_61.01.ckpt. Run following scripts:
bash experiments/predict.shWe provide 2 variants on LiM3D. In network/modules/cylinder3d.py,
- Normal SparseConv3d: Un-comment Line 5 (
from network.modules.sparse_convolution import *) - SDSC: Un-comment Line 8 in (
from network.modules.sds_convolution import *)
Our SDSC module is much more efficient with IPU (Intelligence Processing Unit) + PopTorch than normal GPU.
The SDSC module uses sparse group convolution (official SpConv), which is limited by memory bandwidth. Modern hardware depends on vector instructions for efficient dot product computations. Inefficiencies occur when these instructions aren’t fully utilized, causing potential FLOP wastage. Furthermore, if data isn’t immediately available to the compute engine, extra cycles are required for data transfer. This limitation’s impact is primarily influenced by memory bandwidth, which is likely the main constraint for the efficiency of our sparse depthwise and small-group convolutions.
Based on the above issues, we recommend using IPU (Intelligence Processing Unit) for SDSC training. IPUs are specifically designed to handle sparse data efficiently, with architecture that maximizes the utilization of vector instructions and reduces FLOP wastage. Their high in-processor memory bandwidth and low-latency memory access ensure that data is readily available to the compute engine, minimizing additional cycles for data transfer. This makes IPUs highly suitable for sparse depthwise and small-group convolutions, enhancing overall training efficiency.
As the future research direction, we are attempting to optimize the SDSC module with gpu-optimized architecture (refer to https://github.com/MegEngine/RepLKNet, https://github.com/dvlab-research/spconv-plus, etc), aiming to achieve breakthroughs and balance in accuracy, FLOPs, parameter size, and actual training time on normal GPU.
If you are making use of this work in any way, you must please reference the following paper in any report, publication, presentation, software release or any other associated materials:
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation (Li Li, Hubert P. H. Shum and Toby P. Breckon), In IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), 2023. [homepage] [pdf] [video] [poster]
@InProceedings{li23lim3d,
title = {Less Is {{More}}: {{Reducing Task}} and {{Model Complexity}} for {{3D Point Cloud Semantic Segmentation}}},
author = {Li, Li and Shum, Hubert P. H. and Breckon, Toby P.},
keywords = {point cloud, semantic segmentation, sparse convolution, depthwise separable convolution, autonomous driving},
year = {2023},
month = June,
publisher = {{IEEE}},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}We would like to additionally thank the authors the open source codebase ScribbleKITTI, Cylinder3D, and U2PL.