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

2025-12-09

AI Summary

Addressing the challenges of high computational cost and inability to produce metric-scale depth in full-surround monocular depth estimation (FSMDE), this paper proposes a scale-consistency-oriented knowledge distillation framework. Methodologically, we introduce two novel distillation strategies: cross-interaction knowledge distillation to enhance scale invariance, and view-relational knowledge distillation to enforce multi-view depth consistency. Additionally, we integrate a classification-based depth binning scheme with a hybrid regression architecture to enable efficient distillation from a heavy teacher model to a lightweight student network. Evaluated on DDAD and nuScenes benchmarks, our approach significantly outperforms both conventional supervised methods and state-of-the-art distillation techniques. It achieves real-time inference speed while markedly improving metric depth accuracy—marking the first FSMDE solution that jointly delivers high efficiency, strong scale consistency, and geometrically plausible multi-view depth.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Addresses high computational cost in full surround monocular depth estimation
• Solves difficulty in estimating metric-scale depth from relative predictions
• Enhances cross-view depth consistency for multi-camera systems

Innovation

• Knowledge distillation transfers foundation model depth to lightweight network
• Hybrid regression combines distillation with depth binning for scale consistency
• View-relational distillation encodes cross-view structural relationships for consistency