Monocular Visual 8D Pose Estimation for Articulated Bicycles and Cyclists
Eduardo R. Corral-Soto, Yang Liu, Yuan Ren, Bai Dongfeng, Liu Bingbing

TL;DR
This paper introduces a novel monocular 8D pose estimation method for articulated bicycles and cyclists, enabling more precise pose and travel direction estimation from a single RGB image, crucial for autonomous driving safety.
Contribution
The work presents the first category-level 8D pose estimation approach for articulated bicycles, estimating steering and pedal angles alongside position and orientation from a single image.
Findings
Achieves competitive accuracy compared to 6D pose estimators.
Jointly estimates 8D pose and keypoints for articulated bicycles.
Generalizes well from synthetic to real images.
Abstract
In Autonomous Driving, cyclists belong to the safety-critical class of Vulnerable Road Users (VRU), and accurate estimation of their pose is critical for cyclist crossing intention classification, behavior prediction, and collision avoidance. Unlike rigid objects, articulated bicycles are composed of movable rigid parts linked by joints and constrained by a kinematic structure. 6D pose methods can estimate the 3D rotation and translation of rigid bicycles, but 6D becomes insufficient when the steering/pedals angles of the bicycle vary. That is because: 1) varying the articulated pose of the bicycle causes its 3D bounding box to vary as well, and 2) the 3D box orientation is not necessarily aligned to the orientation of the steering which determines the actual intended travel direction. In this work, we introduce a method for category-level 8D pose estimation for articulated bicycles and…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Multimodal Machine Learning Applications · Robotics and Sensor-Based Localization
