PMPF: Point-Cloud Multiple-Pixel Fusion-Based 3D Object Detection for Autonomous Driving

Engineering notes

Extensive experiments on the KITTI 3D object detection benchmark show that PMPF vastly improves upon most of the LiDAR-only detectors, e.g., PointPillars, SECOND, CIA-SSD, SE-SSD four state-of-the-art one-stage detectors, and PointRCNN, PV-RCNN, Part-A2 three two-stage detectors.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF, point-cloud multiple-pixel fusion, for 3D object detection. PMPF projects the point cloud data onto the image plane, where the region pixels are processed to correspond with the points and decorated to the point cloud data, such that the fused point cloud data can be applied to LiDAR-only detectors with autoencoders. PMPF is a plug-and-play, decoupled multi-sensor fusion detection framework with low latency. Extensive experiments on the KITTI 3D object detection benchmark show that PMPF vastly improves upon most of the LiDAR-only detectors, e.g., PointPillars, SECOND, CIA-SSD, SE-SSD four state-of-the-art one-stage detectors, and PointRCNN, PV-RCNN, Part-A2 three two-stage detectors.

Links and sources

• Official / arXiv page
• PDF from original source

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