Real-time Binocular 3D Reconstruction for Enhanced Depth Perception and Virtual Reality

Abstract

Motivated by the escalating demand for increasingly immersive and realistic virtual reality experiences, the enhancement of depth perception via precise 3D reconstruction has emerged as a fundamental requirement. In response to existing challenges such as depth ambiguity, calibration inaccuracies, and occlusion handling, this paper introduces a novel real-time binocular 3D reconstruction framework based on the integration of feature-based and learning-based process. Specifically, the proposed approach features a hybrid neural network improving depth accuracy in textureless areas and a deep learning model incorporating local geometric priors to refine disparity maps, thereby enhancing depth prediction in occluded regions of the inputs. Experimental results demonstrate significant improvements in the accuracy and efficiency of real-time binocular 3D reconstructions, with the system capable of producing high-quality, detailed models in real time. This work not only addresses some key limitations of current technologies but also significantly advances the potential for more sophisticated and immersive VR applications.