Sensor Domain Adaptation for 3D Object Detection via LiDAR Super-Resolution

Abstract. LiDAR-based perception models’ performance can degrade sharply when applied to data from sensors different to those they were trained on. LiDAR super-resolution aims to enhance sparse point clouds from low-cost sensors. This can help to bridge the sensor domain gap to higher resolution LiDAR. Prior work has primarily focused on reconstruction quality metrics for super-resolution with limited evaluation of downstream perception tasks. We address this gap by conducting a systematic analysis of how super-resolution quality impacts 3D object detection performance. We evaluate detection capability through zero-shot transfer experiments on the KITTI object dataset. Four representative detectors (SECOND, PointPillars, PV-RCNN, PointRCNN) trained on high-resolution data are directly applied to super-resolved low-resolution data without fine-tuning. Results reveal a critical insight: reconstruction improvements yield vastly different detection gains across architectures. PointPillars shows minimal improvement until reaching high reconstruction quality, then performance improves significantly. In contrast, PV-RCNN exhibits steady gains throughout. The highest-quality reconstruction closes up to 86% of the performance gap and enables detection in safety-critical scenarios, including distant vehicles and small pedestrians, where lower-quality methods fail entirely. This work establishes that LiDAR super-resolution effectiveness depends on both reconstruction quality and detector architecture.