Shape from Polarization for Complex Scenes in the Wild (2112.11377v3)

Abstract: We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. Existing shape from polarization (SfP) works mainly focus on estimating the normal of a single object rather than complex scenes in the wild. A key barrier to high-quality scene-level SfP is the lack of real-world SfP data in complex scenes. Hence, we contribute the first real-world scene-level SfP dataset with paired input polarization images and ground-truth normal maps. Then we propose a learning-based framework with a multi-head self-attention module and viewing encoding, which is designed to handle increasing polarization ambiguities caused by complex materials and non-orthographic projection in scene-level SfP. Our trained model can be generalized to far-field outdoor scenes as the relationship between polarized light and surface normals is not affected by distance. Experimental results demonstrate that our approach significantly outperforms existing SfP models on two datasets. Our dataset and source code will be publicly available at https://github.com/ChenyangLEI/sfp-wild

• The paper introduces a novel dataset specifically for scene-level polarization normal estimation, filling a critical research gap.
• The proposed self-attention framework leverages physics-based priors to resolve polarization ambiguities in complex, outdoor scenes.
• Experimental results demonstrate robust performance, outperforming baseline methods in far-field surface normal estimation using polarization cues.

Shape from Polarization for Complex Scenes in the Wild: An Expert Overview

The paper "Shape from Polarization for Complex Scenes in the Wild" addresses advanced methodologies for surface normal estimation at the scene level using polarization data. This work departs from traditional Shape from Polarization (SfP) techniques which commonly focus on isolated object normal estimation. The authors tackle complex, real-world environments where conventional SfP methods struggle due to limited data and inherent ambiguities of polarization.

Contribution and Dataset

The paper offers significant contributions through the introduction of a novel dataset specifically intended for scene-level SfP. This dataset includes real-world scenes paired with high-resolution ground-truth normal maps, filling a notable gap in current research where existing datasets either focus on synthetic environments or isolate single objects. The dataset provided allows for the training of machine learning models capable of interpreting normal maps in much more intricate scenes, hence pushing forward the capability of SfP technologies beyond academic or indoor settings.

Methodological Advancements

To address polarization ambiguities in complex scenes, the authors propose innovative computational techniques. A unique learning-based framework equipped with a multi-head self-attention module and viewing direction encoding is developed. This framework capitalizes on physics-based priors, enabling the resolving of polarization ambiguities that arise due to varied materials and projection inconsistencies. The strength of this approach is validated by outperforming existing SfP models across two datasets, evidencing its robustness and applicability in diverse settings.

Experimental Analysis

Substantial experimental evaluations depict the remarkable superiority of the proposed model over baseline methods. The innovations outlined in the authors' approach not only demonstrate improved performance metrics but also reveal enhanced adaptability to far-field outdoor scenes—a noteworthy characteristic given typical limitations of active sensors in such environments. The empirical results further emphasize the efficacy of this approach in deriving surface normals from single polarization images without compromising distance parameters.

Implications and Future Directions

Practically, the advancements in scene-level normal estimation have potential applications in domains such as autonomous navigation, 3D scene reconstruction, and enhanced environmental mapping. Theoretically, this paper sets the stage for further exploration into SfP models capable of leveraging polarization cues for comprehensive scene understanding.

Future developments in the field could aim at refining polarization prior frameworks and enhancing neural architectures to interpret fine details in polarized light data more efficiently. Furthermore, understanding and mitigating remaining ambiguities in polarization data will be critical to advancing the reliability and accuracy of SfP applications.

Overall, this research makes a significant stride toward enabling more sophisticated interaction with real-world scenes using polarization-based techniques, setting a benchmark for future studies in the domain.