  Existing single-stage detectors for locating objects in point clouds often
treat object localization and category classification as separate tasks, so the
localization accuracy and classification confidence may not well align. To
address this issue, we present a new single-stage detector named the Confident
IoU-Aware Single-Stage object Detector (CIA-SSD). First, we design the
lightweight Spatial-Semantic Feature Aggregation module to adaptively fuse
high-level abstract semantic features and low-level spatial features for
accurate predictions of bounding boxes and classification confidence. Also, the
predicted confidence is further rectified with our designed IoU-aware
confidence rectification module to make the confidence more consistent with the
localization accuracy. Based on the rectified confidence, we further formulate
the Distance-variant IoU-weighted NMS to obtain smoother regressions and avoid
redundant predictions. We experiment CIA-SSD on 3D car detection in the KITTI
test set and show that it attains top performance in terms of the official
ranking metric (moderate AP 80.28%) and above 32 FPS inference speed,
outperforming all prior single-stage detectors. The code is available at
https://github.com/Vegeta2020/CIA-SSD.
