Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation

• URL: http://arxiv.org/abs/2603.01765v2
• Date: Tue, 03 Mar 2026 05:43:24 GMT
• Title: Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
• Authors: Minseok Seo, Wonjun Lee, Jaehyuk Jang, Changick Kim,
• Abstract summary: We show that adapting only the decoder is sufficient for effective test-time optimization.<n>We propose a lightweight test-time adaptation method that updates only this low-dimensional subspace using sparse depth supervision.
• Score: 35.24180364395977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot depth completion has gained attention for its ability to generalize across environments without sensor-specific datasets or retraining. However, most existing approaches rely on diffusion-based test-time optimization, which is computationally expensive due to iterative denoising. Recent visual-prompt-based methods reduce training cost but still require repeated forward--backward passes through the full frozen network to optimize input-level prompts, resulting in slow inference. In this work, we show that adapting only the decoder is sufficient for effective test-time optimization, as depth foundation models concentrate depth-relevant information within a low-dimensional decoder subspace. Based on this insight, we propose a lightweight test-time adaptation method that updates only this low-dimensional subspace using sparse depth supervision. Our approach achieves state-of-the-art performance, establishing a new Pareto frontier between accuracy and efficiency for test-time adaptation. Extensive experiments on five indoor and outdoor datasets demonstrate consistent improvements over prior methods, highlighting the practicality of fast zero-shot depth completion.

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