MVS-TTA: Test-Time Adaptation for Multi-View Stereo via Meta-Auxiliary Learning

• URL: http://arxiv.org/abs/2511.18120v1
• Date: Sat, 22 Nov 2025 16:52:47 GMT
• Title: MVS-TTA: Test-Time Adaptation for Multi-View Stereo via Meta-Auxiliary Learning
• Authors: Hannuo Zhang, Zhixiang Chi, Yang Wang, Xinxin Zuo,
• Abstract summary: MVS-TTA is an efficient test-time adaptation framework for learning-based MVS methods.<n>We introduce a meta-auxiliary learning strategy to train the model to benefit from auxiliary-task-based updates explicitly.<n>Our framework is model-agnostic and can be applied to a wide range of MVS methods with minimal architectural changes.
• Score: 15.25971188918359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent learning-based multi-view stereo (MVS) methods are data-driven and have achieved remarkable progress due to large-scale training data and advanced architectures. However, their generalization remains sub-optimal due to fixed model parameters trained on limited training data distributions. In contrast, optimization-based methods enable scene-specific adaptation but lack scalability and require costly per-scene optimization. In this paper, we propose MVS-TTA, an efficient test-time adaptation (TTA) framework that enhances the adaptability of learning-based MVS methods by bridging these two paradigms. Specifically, MVS-TTA employs a self-supervised, cross-view consistency loss as an auxiliary task to guide inference-time adaptation. We introduce a meta-auxiliary learning strategy to train the model to benefit from auxiliary-task-based updates explicitly. Our framework is model-agnostic and can be applied to a wide range of MVS methods with minimal architectural changes. Extensive experiments on standard datasets (DTU, BlendedMVS) and a challenging cross-dataset generalization setting demonstrate that MVS-TTA consistently improves performance, even when applied to state-of-the-art MVS models. To our knowledge, this is the first attempt to integrate optimization-based test-time adaptation into learning-based MVS using meta-learning. The code will be available at https://github.com/mart87987-svg/MVS-TTA.

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