ETA: Energy-based Test-time Adaptation for Depth Completion
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911111392002048 |
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| author | Chung, Younjoon Park, Hyoungseob Rim, Patrick Zhang, Xiaoran He, Jihe Zeng, Ziyao Cicek, Safa Hong, Byung-Woo Duncan, James S. Wong, Alex |
| author_facet | Chung, Younjoon Park, Hyoungseob Rim, Patrick Zhang, Xiaoran He, Jihe Zeng, Ziyao Cicek, Safa Hong, Byung-Woo Duncan, James S. Wong, Alex |
| contents | We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when transferred to ``target'' data captured in novel environmental conditions due to a covariate shift. The crux of our method lies in quantifying the likelihood of depth predictions belonging to the source data distribution. The challenge is in the lack of access to out-of-distribution (target) data prior to deployment. Hence, rather than making assumptions regarding the target distribution, we utilize adversarial perturbations as a mechanism to explore the data space. This enables us to train an energy model that scores local regions of depth predictions as in- or out-of-distribution. We update the parameters of pretrained depth completion models at test time to minimize energy, effectively aligning test-time predictions to those of the source distribution. We call our method ``Energy-based Test-time Adaptation'', or ETA for short. We evaluate our method across three indoor and three outdoor datasets, where ETA improve over the previous state-of-the-art method by an average of 6.94% for outdoors and 10.23% for indoors. Project Page: https://fuzzythecat.github.io/eta. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ETA: Energy-based Test-time Adaptation for Depth Completion Chung, Younjoon Park, Hyoungseob Rim, Patrick Zhang, Xiaoran He, Jihe Zeng, Ziyao Cicek, Safa Hong, Byung-Woo Duncan, James S. Wong, Alex Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when transferred to ``target'' data captured in novel environmental conditions due to a covariate shift. The crux of our method lies in quantifying the likelihood of depth predictions belonging to the source data distribution. The challenge is in the lack of access to out-of-distribution (target) data prior to deployment. Hence, rather than making assumptions regarding the target distribution, we utilize adversarial perturbations as a mechanism to explore the data space. This enables us to train an energy model that scores local regions of depth predictions as in- or out-of-distribution. We update the parameters of pretrained depth completion models at test time to minimize energy, effectively aligning test-time predictions to those of the source distribution. We call our method ``Energy-based Test-time Adaptation'', or ETA for short. We evaluate our method across three indoor and three outdoor datasets, where ETA improve over the previous state-of-the-art method by an average of 6.94% for outdoors and 10.23% for indoors. Project Page: https://fuzzythecat.github.io/eta. |
| title | ETA: Energy-based Test-time Adaptation for Depth Completion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.05989 |