Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

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 meth...

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Bibliographic Details
Published in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 5776 - 5783
Main Authors Li, Chengyao, Ku, Jason, Waslander, Steven L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.10.2020
Subjects
Autonomous vehicles
Benchmark testing
Detectors
Estimation
Object detection
Pipelines
Three-dimensional displays
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS45743.2020.9341188