A Dataset of Watch and Wristband for Deep Learning based Multi-view Stereo 3D Model Reconstruction

Abstract

Multi-view stereo (MVS) 3D reconstruction based on deep learning has achieved great success, however, it requires a very high quality and quantity of datasets compared with other computer vision tasks. Current 3D datasets have great limitations in the reconstruction of industrial products, including low accuracy, few types of styles, and few pairwise image models. In this paper, we introduce a new dataset for MVS 3D Model Reconstruction, focusing on the watch wristband category. Better than the existing available open-source watch and wristband dataset, ours contains more than 1k multi-view high-resolution images and high-precision 3D models, covering cartoon, mechanical, vintage, etc. Most importantly, ours can be used directly for deep learning-based MVS 3D reconstruction, because besides three views of real images, we drew line sketches of the three views, and then match them to the high-precision 3D model one by one. At last, we train the MVS network based on deep learning with our dataset as input and supervision. The experiments show that we achieve significant results, and verify the effectiveness of reconstruction in the watch wristband category.

Keywords: Multi, view Stereo, 3D Data, Watch and Wristband, Deep Learning

DOI: 10.54941/ahfe1003624