  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.
