6DoF Pose estimation has been gaining increased importance in vision for over a decade, however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect, we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras, HDR, and algorithms targeted at reliable, high-accuracy industrial automation. Specifically, we capture 2,300 physical scenes of 20 industrial parts covering a 1m × 1m × 0.5m working volume, resulting in over 100,000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting, we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100,000 lux. We also present, validate, and analyze robot consistency, an evaluation method targeted at scalable, high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.

Segmentation of transparent objects is a hard, open problem in computer vision. Transparent objects lack texture of their own, adopting instead the texture of scene background. This paper reframes the problem of transparent object segmentation into the realm of light polarization, i.e., the rotation of light waves. We use a polarization camera to capture multi-modal imagery and couple this with a unique deep learning backbone for processing polarization input data. Our method achieves instance segmentation on cluttered, transparent objects in various scene and background conditions, demonstrating an improvement over traditional image-based approaches. As an application we use this for robotic bin picking of transparent objects.

Coarse depth maps can be enhanced by using the shape information from polarization cues. We propose a framework to combine surface normals from polarization (hereafter polarization normals) with an aligned depth map. Polarization normals have not been used for depth enhancement before. This is because polarization normals suffer from physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We propose a framework to overcome these key challenges, allowing the benefits of polarization to be used to enhance depth maps. Our results demonstrate improvement with respect to state-of-the-art 3D reconstruction techniques.