Abstract
Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.
Highlights
Close-range photogrammetric techniques proved to be accurate and reliable 3D non-contact measurement in many applications beginning with industrial ones and spanning to anthropology and cultural heritage (Bosemann, 2011, Remondino, 2011, Knyaz and Maksimov, 2014)
Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation
The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture
Summary
Close-range photogrammetric techniques proved to be accurate and reliable 3D non-contact measurement in many applications beginning with industrial ones and spanning to anthropology and cultural heritage (Bosemann, 2011, Remondino, 2011, Knyaz and Maksimov, 2014). Many methods were proposed to compensate error in stereo matching for structured light systems (Curless and Levoy, 1995, Wang et al, 2016, Taylor, 2012, O’Toole et al, 2014, Wang and Feng, 2014, Chen and Shen, 2018, Bian and Liu, 2016, Knyaz, 2010) While these methods reduce surface distance error between reconstructed and the ground truth models, they could not eliminate the discrepancies caused by uneven texture brightness. That our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels. The inference time of our LineMatchGAN is 1 second for input with a resolution of 1024×768 pixels using NVIDIA Jetson TX2 GPU Such computational efficiency allows using the proposed pipeline to improve the accuracy of existing hand-held scanners for on-line data processing.
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