Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
Beijing Institute of Technology · Vi Technology (United States) · +1 more institution
Abstract
In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we systematically develop an object-centric temporal mechanism. The model is performed in an online manner and the long-term historical information is propagated through object queries frame by frame. Besides, we introduce a motion-aware layer normalization to model the movement of the objects. StreamPETR achieves significant performance improvements only with negligible computation cost, compared to the single-frame baseline. On the standard nuScenes benchmark, it is the first online multi-view method that achieves comparable performance…
Citation impact
- FWCI
- 24.64
- Percentile
- 100%
- References
- 79
Authors
5Topics & keywords
- Computer science
- Benchmark (surveying)
- Frame (networking)
- Normalization (sociology)
- Object (grammar)
- Artificial intelligence
- Computer vision
- Object detection