Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey

TL;DR

This survey reviews over 60 vision-based 3D detection methods for autonomous driving, emphasizing surround-view approaches, analyzing current trends, challenges, and future research directions in 3D perception using camera data.

Contribution

It provides a comprehensive analysis of existing vision-based 3D detection techniques, highlighting the shift towards surround-view methods and discussing future research challenges.

Findings

Trend towards surround-view image methods

Identification of current technique shortcomings

Future research directions proposed

Abstract

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.