Robust Depth Enhancement via Polarization Prompt Fusion Tuning

• URL: http://arxiv.org/abs/2404.04318v1
• Date: Fri, 5 Apr 2024 17:55:33 GMT
• Title: Robust Depth Enhancement via Polarization Prompt Fusion Tuning
• Authors: Kei Ikemura, Yiming Huang, Felix Heide, Zhaoxiang Zhang, Qifeng Chen, Chenyang Lei,
• Abstract summary: We present a framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance, we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets.
• Score: 112.88371907047396
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios, such as in the presence of transparent or reflective objects. In this work, we present a general framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Previous polarization-based depth enhancement methods focus on utilizing pure physics-based formulas for a single sensor. In contrast, our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance, we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets, as the size of the polarization dataset is limited to train a strong model from scratch. We conducted extensive experiments on a public dataset, and the results demonstrate that the proposed method performs favorably compared to existing depth enhancement baselines. Code and demos are available at https://lastbasket.github.io/PPFT/.

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