30/11/2020

IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image

Dingfu Zhou, Xibin Song, Yuchao Dai, Junbo Yin, Feixiang Lu, Miao Liao, Jin Fang, Liangjun Zhang

Keywords:

Abstract Paper Similar Papers

Abstract: 3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous and challenging as single image depth estimation is already an ill-posed problem. In this paper, we propose an instance-aware approach to aggregate useful information for improving the accuracy of 3D object detection with the following contributions. First, an instance-aware feature aggregation (IAFA) module is proposed to collect local and global features for 3D bounding boxes regression. Second, we empirically find that the spatial attention module can be well learned by taking coarse-level instance annotations as a supervision signal. The proposed module has significantly boosted the performance of the baseline method on both 3D detection and 2D bird-eye's view of vehicle detection among all three categories. Third, our proposed method outperforms all single image-based approaches (even these methods trained with depth as auxiliary inputs) and achieves state-of-the-art 3D detection performance on the KITTI benchmark.

The video of this talk cannot be embedded. You can watch it here:
https://accv2020.github.io/miniconf/poster_20.html
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at ACCV 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 13:21 
 0
 0
 0
 0
 14:24 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 10:52 
 0
 0
 0
 0
 12:29 
 0
 0
 0
 0
 14:39 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 16:49 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 0:51 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 9:45 
 0
 0
 0
 0
 9:20 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 2:32 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:06 
 0
 0
 0
 0
 3:06 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 0:57 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 4:49 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 13:34 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:02 
 0
 0
 0
 0
 2:46 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 8:30 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 5:05 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:28