DepthMamba with Adaptive Fusion

Zelin Meng; Zhichen Wang

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arXiv · 2412.19964

DepthMamba with Adaptive Fusion

Zelin Meng·Zhichen Wang

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Abstract

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To tackle this challenge, we propose a two-branch network architecture which fuses the depth estimation results of single-view and multi-view branch. In specific, we introduced mamba to serve as feature extraction backbone and propose an attention-based fusion methods which adaptively select the most robust estimation results between the two branches. Thus, the proposed method can perform well on some challenging scenes including dynamic objects, texture-less regions, etc. Ablation studies prove the effectiveness of the backbone and fusion method, while evaluation experiments on challenging benchmarks (KITTI and DDAD) show that the proposed method achieves a competitive performance compared to the state-of-the-art methods.

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BibTeX RIS

Zelin Meng, Zhichen Wang. 2024-12-28. DepthMamba with Adaptive Fusion. https://arxiv.org/abs/2412.19964

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