Paper
Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
Authors
Minseok Seo, Wonjun Lee, Jaehyuk Jang, Changick Kim
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.01765v1</id>\n <title>Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation</title>\n <updated>2026-03-02T11:45:19Z</updated>\n <link href='https://arxiv.org/abs/2603.01765v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.01765v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-02T11:45:19Z</published>\n <arxiv:comment>17 pages, 7 figures [We achieved a new Pareto frontier in test-time depth completion.]</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Minseok Seo</name>\n </author>\n <author>\n <name>Wonjun Lee</name>\n </author>\n <author>\n <name>Jaehyuk Jang</name>\n </author>\n <author>\n <name>Changick Kim</name>\n </author>\n </entry>"
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