Test-Time Adaptation for Depth Completion

• URL: http://arxiv.org/abs/2402.03312v4
• Date: Mon, 27 May 2024 16:39:45 GMT
• Title: Test-Time Adaptation for Depth Completion
• Authors: Hyoungseob Park, Anjali Gupta, Alex Wong,
• Abstract summary: It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass.
• Score: 9.304152205375757
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (DA), may require the source data on which the model was trained (often not available), while others, i.e., source-free DA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%.

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