High-quality Voxel Reconstruction from Stereoscopic Images

Abstract

Volumetric reconstruction from one or multiple RGB images has shown significant advances in recent years, but the approaches used so far do not take advantage of stereoscopic features such as distance blur, perspective disparity, textures, etc. that are useful to shape the object volumes. Our study is to evaluate a convolutional neural network architecture for reconstruction of 128³ voxel models from 960 pairs of stereoscopic images. The preliminary results show an 80% of coincidence with the original models in 2 categories using the Intersection over Union metric. These results indicate that good reconstructions can be made from a small dataset. This will reduce the time and memory usage for this task.