Learning Ego 3D Representation as Ray Tracing

• URL: http://arxiv.org/abs/2206.04042v1
• Date: Wed, 8 Jun 2022 17:55:50 GMT
• Title: Learning Ego 3D Representation as Ray Tracing
• Authors: Jiachen Lu, Zheyuan Zhou, Xiatian Zhu, Hang Xu, Li Zhang
• Abstract summary: We present a novel end-to-end architecture for ego 3D representation learning from unconstrained camera views. Inspired by the ray tracing principle, we design a polarized grid of "imaginary eyes" as the learnable ego 3D representation. We show that our model outperforms all state-of-the-art alternatives significantly.
• Score: 42.400505280851114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A self-driving perception model aims to extract 3D semantic representations from multiple cameras collectively into the bird's-eye-view (BEV) coordinate frame of the ego car in order to ground downstream planner. Existing perception methods often rely on error-prone depth estimation of the whole scene or learning sparse virtual 3D representations without the target geometry structure, both of which remain limited in performance and/or capability. In this paper, we present a novel end-to-end architecture for ego 3D representation learning from an arbitrary number of unconstrained camera views. Inspired by the ray tracing principle, we design a polarized grid of "imaginary eyes" as the learnable ego 3D representation and formulate the learning process with the adaptive attention mechanism in conjunction with the 3D-to-2D projection. Critically, this formulation allows extracting rich 3D representation from 2D images without any depth supervision, and with the built-in geometry structure consistent w.r.t. BEV. Despite its simplicity and versatility, extensive experiments on standard BEV visual tasks (e.g., camera-based 3D object detection and BEV segmentation) show that our model outperforms all state-of-the-art alternatives significantly, with an extra advantage in computational efficiency from multi-task learning.

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