Confidence Guided Stereo 3D Object Detection with Split Depth Estimation
This work improves 3D object detection for autonomous driving systems, though it is incremental as it builds on existing stereo-based methods.
The paper tackles the performance gap in stereo-based 3D object detection for autonomous driving by addressing inaccurate depth estimation, particularly for foreground objects, and proposes CG-Stereo, which uses separate decoders for foreground and background pixels and integrates depth confidence as a soft attention mechanism, outperforming all state-of-the-art stereo-based methods on the KITTI benchmark.
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.