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Papers › Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

11 Mar 2020arXiv:2003.05505archive 2025-07-28

Chengyao Li, Jason Ku, Steven L. Waslander

Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods is still considerable. Accurate depth estimation is crucial to the performance of stereo-based 3D object detection methods, particularly for those pixels associated with objects in the foreground. Moreover, stereo-based methods suffer from high variance in the depth estimation accuracy, which is often not considered in the object detection pipeline. To tackle these two issues, we propose CG-Stereo, a confidence-guided stereo 3D object detection pipeline that uses separate decoders for foreground and background pixels during depth estimation, and leverages the confidence estimation from the depth estimation network as a soft attention mechanism in the 3D object detector. Our approach outperforms all state-of-the-art stereo-based 3D detectors on the KITTI benchmark.

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Tasks

3D Object Detection3D Object Detection From Stereo ImagesAutonomous DrivingDepth EstimationObjectObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Object Detection From Stereo Images KITTI Cars Moderate CG-Stereo AP75 53.58 #4 of 12 Archive leaderboard report
3D Object Detection From Stereo Images KITTI Pedestrians Moderate CG-Stereo AP50 24.31 #4 of 6 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.

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