3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Häne, Christian; Heng, Lionel; Lee, Gim Hee; Fraundorfer, Friedrich; Furgale, Paul; Sattler, Torsten; Pollefeys, Marc

doi:10.48550/arxiv.1708.09839

Cited by 1 publication

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“…With the rapid development of autonomous driving [1], the 3D perception has drawn increasing attention and shown great potential in the vehicle-mounted system [2,3], which is critical to localization [4,5], planning, and obstacle avoidance [6,7], etc., as shown in Figure 1. Among existing methods [8,9,10], they usually employ the homography transformation [8] of the wheel grounding points obtained from the wheel's detection results to estimate the localization of target vehicles with limited computing ability in the low cost environment.…”

Section: Introductionmentioning

confidence: 99%

DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

Wu¹,

Wang²,

Wang³

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment. However, there are currently two main challenges about the above relationship prediction: i) The traditional heuristic methods based on IoU can hardly deal with the traffic jam scenarios for the occlusion. ii) It is difficult to establish an efficient applicable solution for the vehicle-mounted system. To address these issues, we propose an innovative relationship prediction method, namely DeepWORD, by designing a graph convolution network (GCN). Specifically, we utilize the feature maps with local correlation as the input of nodes to improve the information richness. Besides, we introduce the graph attention network (GAT) to dynamically amend the prior estimation deviation. Furthermore, we establish an annotated owner-member relationship dataset called WORD as a large-scale benchmark, which will be available soon. The experiments demonstrate that our solution achieves state-ofthe-art accuracy and real-time in practice.

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