Mobile Robotic Multi-View Photometric Stereo
Suryansh Kumar

TL;DR
This paper introduces a mobile robotic system for Multi-View Photometric Stereo (MVPS) that enables detailed 3D object reconstruction without complex calibration, using a supervised learning approach for efficient and accurate results.
Contribution
It presents a novel mobile robotic MVPS system with an incremental algorithm leveraging supervised learning for surface normal and depth prediction, improving efficiency and usability.
Findings
Nearly 100 times faster than existing MVPS methods
Achieves high-frequency surface detail recovery
Operates effectively on objects with unknown reflectance
Abstract
Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D acquisition of an object from images. Despite its outstanding results on diverse material objects, a typical MVPS experimental setup requires a well-calibrated light source and a monocular camera installed on an immovable base. This restricts the use of MVPS on a movable platform, limiting us from taking MVPS benefits in 3D acquisition for mobile robotics applications. To this end, we introduce a new mobile robotic system for MVPS. While the proposed system brings advantages, it introduces additional algorithmic challenges. Addressing them, in this paper, we further propose an incremental approach for mobile robotic MVPS. Our approach leverages a supervised learning setup to predict per-view surface normal, object depth, and per-pixel uncertainty in model-predicted results. A refined depth map per view is…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Advanced Vision and Imaging · Satellite Image Processing and Photogrammetry
