论文标题
置信度引导的立体声3D对象检测，深度估计
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation
论文作者
论文摘要
准确可靠的3D对象检测对于安全自动驾驶至关重要。尽管有最近的发展，但基于立体声的方法与基于激光雷达的方法之间的性能差距仍然相当大。准确的深度估计对于基于立体声的3D对象检测方法的性能至关重要，特别是对于与前景中对象相关的那些像素。此外，基于立体声的方法的深度估计准确性具有很大的差异，这在对象检测管道中通常不考虑。为了解决这两个问题，我们提出了CG-STEREO，CG-STEREO是一种信心引导的立体3D对象检测管道，该管道在深度估计过程中使用单独的解码器用于前景和背景像素，并利用了深度估计网络作为3D对象检测器中软注意机制的置信度估计。我们的方法的表现优于Kitti基准测试上的所有基于立体声的3D检测器。
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.
