Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey
It provides a comprehensive review for researchers and engineers in autonomous driving, but it is incremental as a survey paper.
This paper surveys vision-based 3D detection methods for autonomous driving, analyzing over 60 papers to identify trends and highlight the shift towards surround-view image-based approaches.
Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) performance is not an easy task using 2D sensor input-data with cameras. In this paper we provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have made detailed analysis of over $60$ papers leveraging Vision BEV detections approaches and highlighted different sub-groups for detailed understanding of common trends. Moreover, we have highlighted how the literature and industry trend have moved towards surround-view image based methods and note down thoughts on what special cases this method addresses. In conclusion, we provoke thoughts of 3D Vision techniques for future research based on shortcomings of the current techniques including the direction of collaborative perception.