Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation

Helmholtz stereopsis is one the versatile techniques for 3D geometry reconstruction from 2D images of objects with unknown and arbitrary reflectance surfaces. HS eliminates the need for surface reflectance, a challenging parameter to measure, based on the Helmholtz reciprocity principle. Its Bayesian formulation using maximum a posteriori (MAP) probability approach has significantly improved reconstruction accuracy of HS method. This framework enables the inclusion of smoothness priors which enforces observations and neighborhood information in the formulation. We used Markov Random Fields (MRF) which is a powerful tool to integrate diverse prior contextual information and solved the MAP-MRF using belief propagation algorithm. We propose a new smoothness function utilizing the normal field integration method for refined depth estimation within the Bayesian framework. Utilizing three pairs of images with different viewpoints, our approach demonstrates superior depth label accuracy compared to conventional Bayesian methods. Experimental results indicate that our proposed method yields a better depth map with reduced RMS error, showcasing its efficacy in improving depth estimation within Helmholtz stereopsis.

Wi2Vi: Generating Video Frames from WiFi CSI Samples

Objects in an environment affect electromagnetic waves. While this effect varies across frequencies, there exists a correlation between them, and a model with enough capacity can capture this correlation between the measurements in different frequencies. In this paper, we propose the Wi2Vi model for associating variations in the WiFi channel state information with video frames. The proposed Wi2Vi system can generate video frames entirely using CSI measurements. The produced video frames by the Wi2Vi provide auxiliary information to the conventional surveillance system in critical circumstances. Our implementation of the Wi2Vi system confirms the feasibility of constructing a system capable of deriving the correlations between measurements in different frequency spectrums.