MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models

• URL: http://arxiv.org/abs/2503.13743v1
• Date: Mon, 17 Mar 2025 21:59:41 GMT
• Title: MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models
• Authors: Johannes Meier, Louis Inchingolo, Oussema Dhaouadi, Yan Xia, Jacques Kaiser, Daniel Cremers,
• Abstract summary: We introduce a novel unsupervised domain adaptation approach, MonoCT, that generates highly accurate pseudo labels for self-supervision.<n>In experiments on six benchmarks, MonoCT outperforms existing SOTA domain adaptation methods by large margins.
• Score: 33.87605068407066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the problem of monocular 3D object detection across different sensors, environments, and camera setups. In this paper, we introduce a novel unsupervised domain adaptation approach, MonoCT, that generates highly accurate pseudo labels for self-supervision. Inspired by our observation that accurate depth estimation is critical to mitigating domain shifts, MonoCT introduces a novel Generalized Depth Enhancement (GDE) module with an ensemble concept to improve depth estimation accuracy. Moreover, we introduce a novel Pseudo Label Scoring (PLS) module by exploring inner-model consistency measurement and a Diversity Maximization (DM) strategy to further generate high-quality pseudo labels for self-training. Extensive experiments on six benchmarks show that MonoCT outperforms existing SOTA domain adaptation methods by large margins (~21% minimum for AP Mod.) and generalizes well to car, traffic camera and drone views.

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