You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking

• Main Authors: Sun, Jiaming, Xie, Yiming, Zhang, Siyu, Chen, Linghao, Zhang, Guofeng, Bao, Hujun, Zhou, Xiaowei
• Format: Conference Proceeding
• Language: English
• Subjects: 3D from multiview and other sensors; Detection and localization in 2D and 3D; Detectors; Memory management; Motion and tracking; Object detection; Pipelines; Scene analysis and understanding; Search problems; Stereo; Three-dimensional displays; Trajectory; Vision for robotics and autonomous vehicles
• ISSN: 2380-7504
• DOI: 10.1109/ICCV48922.2021.00317

Humans are able to continuously detect and track surrounding objects by constructing a spatial-temporal memory of the objects when looking around. In contrast, 3D object detectors in existing tracking-by-detection systems often search for objects in every new video frame from scratch, without fully leveraging memory from previous detection results. In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames. This memory, together with the ego-motion from back-end odometry, guides the detector to achieve more efficient object proposal generation and more accurate object state estimation. The experiments demonstrate the effectiveness of the proposed system and its performance on the ScanNet and KITTI datasets. Moreover, the proposed system produces stable bounding boxes and pose trajectories over time, while being able to handle occluded and truncated objects. Code is available at the project page: https://zju3dv.github.io/UDOLO.