Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles (2009.11975v1)

Published 24 Sep 2020 in cs.CV and eess.IV

Abstract: To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles. The precision of object detection, however, may require significant improvement, especially for objects that are far away or occluded. To address this critical issue for the safety of autonomous vehicles and human beings, we propose a cooperative spatial feature fusion (CoFF) method for autonomous vehicles to effectively fuse feature maps for achieving a higher 3D object detection performance. Specially, CoFF differentiates weights among feature maps for a more guided fusion, based on how much new semantic information is provided by the received feature maps. It also enhances the inconspicuous features corresponding to far/occluded objects to improve their detection precision. Experimental results show that CoFF achieves a significant improvement in terms of both detection precision and effective detection range for autonomous vehicles, compared to previous feature fusion solutions.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (9)
  1. Jingda Guo (4 papers)
  2. Dominic Carrillo (4 papers)
  3. Sihai Tang (4 papers)
  4. Qi Chen (194 papers)
  5. Qing Yang (138 papers)
  6. Song Fu (24 papers)
  7. Xi Wang (275 papers)
  8. Nannan Wang (106 papers)
  9. Paparao Palacharla (9 papers)
Citations (37)

Summary

We haven't generated a summary for this paper yet.