Multi-Task Multi-Sensor Fusion for 3D Object Detection

Abstract

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and BEV object detection, while being real time.

CVPR 2019

References in corpus (3)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Deep Continuous Fusion for Multi-Sensor 3D Object Detection
• HDNET: Exploiting HD Maps for 3D Object Detection

Cited by in corpus (5)

• 3DSSD: Point-based 3D Single Stage Object Detector
• SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
• Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
• Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
• Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration