Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
This work addresses the challenge of reconstructing specular surfaces for applications in computer vision and metrology, representing an incremental advancement in deflectometry techniques.
The paper tackles the problem of reconstructing large, complex mirror-like objects by introducing a deflectometry-based method that uses a CAVE as a pattern generator, achieving a significant gain in coverage in single measurements compared to previous methods.
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.