Scale-invariant and View-relational Representation Learning for Full Surround Monocular Depth

• URL: http://arxiv.org/abs/2512.08700v1
• Date: Tue, 09 Dec 2025 15:17:20 GMT
• Title: Scale-invariant and View-relational Representation Learning for Full Surround Monocular Depth
• Authors: Kyumin Hwang, Wonhyeok Choi, Kiljoon Han, Wonjoon Choi, Minwoo Choi, Yongcheon Na, Minwoo Park, Sunghoon Im,
• Abstract summary: We propose a knowledge distillation strategy that transfers robust depth knowledge from a foundation model to a lightweight FSMDE network.<n>Our method achieves a favorable trade-off between performance and efficiency, meeting real-time requirements.
• Score: 21.216686704928474
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent foundation models demonstrate strong generalization capabilities in monocular depth estimation. However, directly applying these models to Full Surround Monocular Depth Estimation (FSMDE) presents two major challenges: (1) high computational cost, which limits real-time performance, and (2) difficulty in estimating metric-scale depth, as these models are typically trained to predict only relative depth. To address these limitations, we propose a novel knowledge distillation strategy that transfers robust depth knowledge from a foundation model to a lightweight FSMDE network. Our approach leverages a hybrid regression framework combining the knowledge distillation scheme--traditionally used in classification--with a depth binning module to enhance scale consistency. Specifically, we introduce a cross-interaction knowledge distillation scheme that distills the scale-invariant depth bin probabilities of a foundation model into the student network while guiding it to infer metric-scale depth bin centers from ground-truth depth. Furthermore, we propose view-relational knowledge distillation, which encodes structural relationships among adjacent camera views and transfers them to enhance cross-view depth consistency. Experiments on DDAD and nuScenes demonstrate the effectiveness of our method compared to conventional supervised methods and existing knowledge distillation approaches. Moreover, our method achieves a favorable trade-off between performance and efficiency, meeting real-time requirements.

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