Papers › SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

24 Feb 2020arXiv:2002.10111archive 2025-07-28

Zechen Liu, Zizhang Wu, Roland Tóth

Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving. In case of monocular vision, successful methods have been mainly based on two ingredients: (i) a network generating 2D region proposals, (ii) a R-CNN structure predicting 3D object pose by utilizing the acquired regions of interest. We argue that the 2D detection network is redundant and introduces non-negligible noise for 3D detection. Hence, we propose a novel 3D object detection method, named SMOKE, in this paper that predicts a 3D bounding box for each detected object by combining a single keypoint estimate with regressed 3D variables. As a second contribution, we propose a multi-step disentangling approach for constructing the 3D bounding box, which significantly improves both training convergence and detection accuracy. In contrast to previous 3D detection techniques, our method does not require complicated pre/post-processing, extra data, and a refinement stage. Despite of its structural simplicity, our proposed SMOKE network outperforms all existing monocular 3D detection methods on the KITTI dataset, giving the best state-of-the-art result on both 3D object detection and Bird's eye view evaluation. The code will be made publicly available.

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Syntology Ran 2 of 9 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran · fixture could not drive it.

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lzccccc/SMOKE officialmentioned on GitHubpytorchMIT report
destinyls/SMOKE mentioned on GitHubpytorchMIT report
PaddlePaddle/Paddle3D paddleApache-2.0 report

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9 samples harvested; 2 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
1ran · fixture could not drive it
7unverified

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cat lzccccc/SMOKE/smoke/modeling/utils.py official repository ran · fixture could not drive it MIT (permissive) · 6cbf46794989b731 · report
strip_prefix_if_present lzccccc/SMOKE/smoke/utils/model_serialization.py official repository ran · our draft was wrong MIT (permissive) · 5a951495ac113177 · report
affine_transform lzccccc/SMOKE/smoke/modeling/heatmap_coder.py official repository unverified MIT (permissive) · f3636481f50cd9a9 · report
encode_label lzccccc/SMOKE/smoke/modeling/smoke_coder.py official repository unverified MIT (permissive) · d564528b55724ff2 · report
get_3rd_point lzccccc/SMOKE/smoke/modeling/heatmap_coder.py official repository unverified MIT (permissive) · b7a39d17dbb951b8 · report
get_transfrom_matrix lzccccc/SMOKE/smoke/modeling/heatmap_coder.py official repository unverified MIT (permissive) · a46a4b1c0a5a4ac2 · report
nms_hm lzccccc/SMOKE/smoke/layers/utils.py official repository unverified MIT (permissive) · 0389a686f602c24d · report
select_topk lzccccc/SMOKE/smoke/layers/utils.py official repository unverified MIT (permissive) · 22aa597947eab5d5 · report
sigmoid_hm lzccccc/SMOKE/smoke/layers/utils.py official repository unverified MIT (permissive) · a0aad7a92e4801dc · report

Tasks

3D Object DetectionAutonomous NavigationKeypoint EstimationMonocular 3D Object DetectionObjectObject DetectionTranslationobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Monocular 3D Object Detection KITTI Cars Moderate SMOKE AP Medium 9.76 #24 of 29 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

ConvolutionMax PoolingR-CNNSVM

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