Papers › PointPillars: Fast Encoders for Object Detection from Point Clouds

PointPillars: Fast Encoders for Object Detection from Point Clouds

14 Dec 2018CVPR 2019 6arXiv:1812.05784archive 2025-07-28

Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom

Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.

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18 repositories listed; official and paper-mentioned ones first.

nutonomy/second.pytorch officialmentioned in papermentioned on GitHubpytorchMIT report
LKLQQ/pointpillars mentioned on GitHubmindsporeApache-2.0 report
SmallMunich/nutonomy_pointpillars mentioned on GitHubpytorchMIT report
Uuuuuuho/Pillar_on_SoC mentioned on GitHubpytorchMIT report
hongru-seu/PointPilliar mentioned on GitHubmindspore report
hova88/Lidardet mentioned on GitHubpytorch report
krullgit/realsense_3d_detection mentioned on GitHubtf report
nvidia-ai-iot/lidar_ai_solution mentioned on GitHubpytorchNOASSERTION report
sanjeewk/ppnet mentioned on GitHub report
zhulf0804/pointpillars mentioned on GitHubpytorch report
PaddlePaddle/Paddle3D paddleApache-2.0 report
open-mmlab/mmdetection3d pytorchApache-2.0 report

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Code Syntology ran Syntology

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1ran · our draft was wrong
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get_score_thresholds zhulf0804/pointpillars/evaluate.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT (permissive) · a48052245131c33f · report
unpack isl-org/Open3D-ML/ml3d/tf/models/point_pillars.py community (archive-listed) ran · our draft was wrong licence not identified · pointer only · 1354276502be4aa8 · report
create_depth_image airalcorn2/paved2paradise/paved2paradise.py community (archive-listed) unverified MIT (permissive) · 566cdbac52dacce0 · report
create_target_np SmallMunich/nutonomy_pointpillars/second/core/target_ops.py community (archive-listed) unverified MIT (permissive) · d2b64ea77c2d6d9f · report
get_config LKLQQ/pointpillars/src/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 17815b44aafcf555 · report
get_dataset_keys LKLQQ/pointpillars/src/builder/dataset_builder.py community (archive-listed) unverified Apache-2.0 (permissive) · 4967576c687dfdf8 · report
get_params_for_net LKLQQ/pointpillars/src/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 451d86db9edf4584 · report
is_line_segment_cross SmallMunich/nutonomy_pointpillars/second/core/geometry.py community (archive-listed) unverified MIT (permissive) · abc7a1803bf35228 · report
is_line_segment_intersection_jit SmallMunich/nutonomy_pointpillars/second/core/geometry.py community (archive-listed) unverified MIT (permissive) · de9bc527072ac4b0 · report
line_segment_intersection SmallMunich/nutonomy_pointpillars/second/core/geometry.py community (archive-listed) unverified MIT (permissive) · 6a7ad14559c54076 · report
riou_cc SmallMunich/nutonomy_pointpillars/second/core/box_np_ops.py community (archive-listed) unverified MIT (permissive) · 2b3e25e607e98a0f · report
second_box_decode SmallMunich/nutonomy_pointpillars/second/core/box_np_ops.py community (archive-listed) unverified MIT (permissive) · 526cf943db7338d3 · report
second_box_encode SmallMunich/nutonomy_pointpillars/second/core/box_np_ops.py community (archive-listed) unverified MIT (permissive) · f2f3e2a705ddc55f · report
unlevel airalcorn2/paved2paradise/paved2paradise.py community (archive-listed) unverified MIT (permissive) · c61b1e019a047572 · report
unmap SmallMunich/nutonomy_pointpillars/second/core/target_ops.py community (archive-listed) unverified MIT (permissive) · 2902bedb7c18b1a1 · report

Tasks

3D Object DetectionAutonomous DrivingBirds Eye View Object DetectionObjectObject DetectionRobust 3D Object Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Object Detection DAIR-V2X-I PointPillars AP|R40(easy) 63.1 #6 of 9 Archive leaderboard report
3D Object Detection DAIR-V2X-I PointPillars AP|R40(hard) 54.0 #6 of 9 Archive leaderboard report
3D Object Detection DAIR-V2X-I PointPillars AP|R40(moderate) 54.0 #6 of 9 Archive leaderboard report
3D Object Detection KITTI Cars Easy PointPillars AP 79.05% #24 of 26 Archive leaderboard report
3D Object Detection KITTI Cyclists Easy PointPillars AP 75.78% #7 of 12 Archive leaderboard report
3D Object Detection KITTI Cyclists Hard PointPillars AP 52.92% #8 of 12 Archive leaderboard report
3D Object Detection KITTI Cyclists Moderate PointPillars AP 59.07% #8 of 13 Archive leaderboard report
3D Object Detection KITTI Pedestrians Moderate PointPillars AP 41.92% #10 of 12 Archive leaderboard report
3D Object Detection nuScenes LiDAR only PointPillar NDS 45.3 #7 of 7 Archive leaderboard report
3D Object Detection nuScenes LiDAR only PointPillar mAP 30.5 #7 of 7 Archive leaderboard report
Birds Eye View Object Detection KITTI Cars Easy PointPillars AP 88.35% #8 of 9 Archive leaderboard report
Birds Eye View Object Detection KITTI Cars Hard PointPillars AP 79.83 #5 of 8 Archive leaderboard report
Birds Eye View Object Detection KITTI Cars Moderate PointPillars AP 86.1% #6 of 9 Archive leaderboard report
Birds Eye View Object Detection KITTI Cyclists Moderate PointPillars AP 62.25% #3 of 6 Archive leaderboard report
Birds Eye View Object Detection KITTI Pedestrians Moderate PointPillars AP 50.23% #4 of 6 Archive leaderboard report
Robust 3D Object Detection KITTI-C SECOND mean Corruption Error (mCE) 95.93% #3 of 5 Archive leaderboard report
Robust 3D Object Detection KITTI-C PointPillars mean Corruption Error (mCE) 110.67% #5 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SPEED

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