CIA-SSD: Confident IoU-Aware Single Stage Object Detector From Point Cloud (AAAI 2021) [Paper]

Currently state-of-the-art single-stage object detector from point cloud on KITTI Benchmark, running with high speed of 32FPS.

Authors: Wu Zheng, Weiliang Tang, Sijin Chen, Li Jiang, Chi-Wing Fu.

AP on KITTI Dataset

Val Split (11 recall points):

Car  AP:98.97, 90.10, 89.49
bev  AP:90.58, 88.80, 87.88
3d   AP:89.99, 79.88, 78.93
aos  AP:98.90, 89.89, 89.11
car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:98.97, 90.10, 89.49
bev  AP:99.03, 90.22, 89.74
3d   AP:99.00, 90.18, 89.68
aos  AP:98.90, 89.89, 89.11

Test Split: Submission link

You may download the pre-trained model here, which is trained on the train split (3712 samples). Notice that it's normal to obtain the moderate 3D AP as 79.8±0.15 when you train it by your own.

Pipeline

The pipeline of our proposed Confident IoU-Aware Single-Stage object Detector (CIA-SSD). First, we encode the input point cloud (a) with a sparse convolutional network denoted by SPConvNet (b), followed by our spatial-semantic feature aggregation (SSFA) module (c) for robust feature extraction, in which an attentional fusion module (d) is adopted to adaptively fuse the spatial and semantic features. Then, the multi-task head (e) realizes the object classification and localization, with our introduced confidence function (CF) for confidence rectification. In the end, we further formulate the distance-variant IoU-weighted NMS (DI-NMS) for post-processing.

Installation

$ git clone https://github.com/Vegeta2020/CIA-SSD.git
$ cd ./CIA-SSD/det3d/core/iou3d
$ python setup.py install
$ cd ./CIA-SSD
$ python setup.py build develop

For installation of other related packages and data preparation, please follow Det3D

Train and Eval

Please use our code to generate ground truth info .pkl file:

$ python ./CIA-SSD/det3d/datasets/utils/create_gt_database.py

Train the CIA-SSD:

Single GPU
$ python ./CIA-SSD/tools/train.py
Multiple GPU
$ python -m torch.distributed.launch --nproc_per_node=4 ./CIA-SSD/tools/train.py

Evaluate the CIA-SSD:

$ python ./CIA-SSD/tools/test.py

Citation

If you find this work useful in your research, please consider cite:

License

This codebase is released under the Apache licenes.

Acknowledgement

Our code are mainly based on Det3D, thanks for their contributions! We also thank for the reviewers's valuable comments of this paper.