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
this https URL

本研究利用偏振成像提高不准确的深度传感器测量，通过学习策略和神经网络预估深度图，采用 Polarization Prompt Fusion Tuning（PPFT）策略优化 RGB 模型性能，并在公开数据集上验证了优于现有深度增强方法的实验结果。

偏振引导融合调节的鲁棒深度增强

Robust Depth Enhancement via Polarization Prompt Fusion Tuning

Polarization information of the light can provide rich cues for computer
vision and scene understanding tasks, such as the type of material, pose, and
shape of the objects. With the advent of new and cheap polarimetric sensors,
this imaging modality is becoming accessible to a wider public for solving
problems such as pose estimation, 3D reconstruction, underwater navigation, and
depth estimation. However, we observe several limitations regarding the usage
of this sensorial modality, as well as a lack of standards and publicly
available tools to analyze polarization images. Furthermore, although
polarization camera manufacturers usually provide acquisition tools to
interface with their cameras, they rarely include processing algorithms that
make use of the polarization information. In this paper, we review recent
advances in applications that involve polarization imaging, including a
comprehensive survey of recent advances on polarization for vision and robotics
perception tasks. We also introduce a complete software toolkit that provides
common standards to communicate with and process information from most of the
existing micro-grid polarization cameras on the market. The toolkit also
implements several image processing algorithms for this modality, and it is
publicly available on GitHub: this https URL

偏振成像在计算机视觉和场景理解任务中可以提供丰富的线索，如物料类型、姿态和物体形状。本文回顾了涉及偏振成像的应用的最新进展，并介绍了一个完整的软件工具包，用于与市场上大多数现有微网偏振相机进行通信和处理信息。