Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects

TL;DR

This paper presents a novel deflectometry-based method utilizing a CAVE environment for the comprehensive reconstruction of large, complex specular objects, enhancing coverage and accuracy in single measurements.

Contribution

It introduces a new optical encoding scheme and an object detection algorithm tailored for the CAVE setup, enabling improved reconstruction of complex mirror surfaces.

Findings

Significant increase in coverage compared to previous methods

Effective segmentation and camera pose estimation in raw images

Public release of a new dataset for specular surface reconstruction

Abstract

We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available.