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PointPillars: Fast Encoders for Object Detection from Point Clouds

Introduction

[ALGORITHM]

We implement PointPillars and provide the results and checkpoints on KITTI, nuScenes, Lyft and Waymo datasets.

@inproceedings{lang2019pointpillars,
  title={Pointpillars: Fast encoders for object detection from point clouds},
  author={Lang, Alex H and Vora, Sourabh and Caesar, Holger and Zhou, Lubing and Yang, Jiong and Beijbom, Oscar},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12697--12705},
  year={2019}
}

Results

KITTI

Backbone Class Lr schd Mem (GB) Inf time (fps) AP Download
SECFPN Car cyclic 160e 5.4 77.1 model | log
SECFPN 3 Class cyclic 160e 5.5 59.5 model | log

nuScenes

Backbone Lr schd Mem (GB) Inf time (fps) mAP NDS Download
SECFPN 2x 16.4 35.17 49.7 model | log
FPN 2x 16.4 40.0 53.3 model | log

Lyft

Backbone Lr schd Mem (GB) Inf time (fps) Private Score Public Score Download
SECFPN 2x 13.4 13.4
FPN 2x 14.0 14.2

Waymo

Backbone Load Interval Class Lr schd Mem (GB) Inf time (fps) mAP@L1 mAPH@L1 mAP@L2 mAPH@L2 Download
SECFPN 5 Car 2x 7.76 70.2 69.6 62.6 62.1 model | log
SECFPN 5 3 Class 2x 8.12 64.7 57.6 58.4 52.1 model | log
above @ Car 2x 8.12 68.5 67.9 60.1 59.6
above @ Pedestrian 2x 8.12 67.8 50.6 59.6 44.3
above @ Cyclist 2x 8.12 57.7 54.4 55.5 52.4
SECFPN 1 Car 2x 7.76 72.1 71.5 63.6 63.1 log
SECFPN 1 3 Class 2x 8.12 68.8 63.3 62.6 57.6 log
above @ Car 2x 8.12 71.6 71.0 63.1 62.5
above @ Pedestrian 2x 8.12 70.6 56.7 62.9 50.2
above @ Cyclist 2x 8.12 64.4 62.3 61.9 59.9

Note:

  • Metric: For model trained with 3 classes, the average APH@L2 (mAPH@L2) of all the categories is reported and used to rank the model. For model trained with only 1 class, the APH@L2 is reported and used to rank the model.
  • Data Split: Here we provide several baselines for waymo dataset, among which D5 means that we divide the dataset into 5 folds and only use one fold for efficient experiments. Using the complete dataset can boost the performance a lot, especially for the detection of cyclist and pedestrian, where more than 5 mAP or mAPH improvement can be expected.
  • Implementation Details: We basically follow the implementation in the paper in terms of the network architecture (having a stride of 1 for the first convolutional block). Different settings of voxelization, data augmentation and hyper parameters make these baselines outperform those in the paper by about 7 mAP for car and 4 mAP for pedestrian with only a subset of the whole dataset. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
  • License Aggrement: To comply the license agreement of Waymo dataset, the pre-trained models on Waymo dataset are not released. We still release the training log as a reference to ease the future research.