RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception

• URL: http://arxiv.org/abs/2407.10876v2
• Date: Sat, 20 Jul 2024 15:46:44 GMT
• Title: RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
• Authors: Chunliang Li, Wencheng Han, Junbo Yin, Sanyuan Zhao, Jianbing Shen,
• Abstract summary: This paper proposes a novel unified representation, RepVF, which harmonizes the representation of various perception tasks. RepVF characterizes the structure of different targets in the scene through a vector field, enabling a single-head, multi-task learning model. Building upon RepVF, we introduce RFTR, a network designed to exploit the inherent connections between different tasks.
• Score: 64.80760846124858
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational inefficiencies and feature competition between tasks when using traditional multi-task learning approaches. This paper addresses these issues by proposing a novel unified representation, RepVF, which harmonizes the representation of various perception tasks such as 3D object detection and 3D lane detection within a single framework. RepVF characterizes the structure of different targets in the scene through a vector field, enabling a single-head, multi-task learning model that significantly reduces computational redundancy and feature competition. Building upon RepVF, we introduce RFTR, a network designed to exploit the inherent connections between different tasks by utilizing a hierarchical structure of queries that implicitly model the relationships both between and within tasks. This approach eliminates the need for task-specific heads and parameters, fundamentally reducing the conflicts inherent in traditional multi-task learning paradigms. We validate our approach by combining labels from the OpenLane dataset with the Waymo Open dataset. Our work presents a significant advancement in the efficiency and effectiveness of multi-task perception in autonomous driving, offering a new perspective on handling multiple 3D perception tasks synchronously and in parallel. The code will be available at: https://github.com/jbji/RepVF

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