  This paper presents a novel approach for visible-thermal infrared
stereoscopy, focusing on the estimation of disparities of human silhouettes.
Visible-thermal infrared stereo poses several challenges, including occlusions
and differently textured matching regions in both spectra. Finding matches
between two spectra with varying colors, textures, and shapes adds further
complexity to the task. To address the aforementioned challenges, this paper
proposes a novel approach where a high-resolution convolutional neural network
is used to better capture relationships between the two spectra. To do so, a
modified HRNet backbone is used for feature extraction. This HRNet backbone is
capable of capturing fine details and textures as it extracts features at
multiple scales, thereby enabling the utilization of both local and global
information. For matching visible and thermal infrared regions, our method
extracts features on each patch using two modified HRNet streams. Features from
the two streams are then combined for predicting the disparities by
concatenation and correlation. Results on public datasets demonstrate the
effectiveness of the proposed approach by improving the results by
approximately 18 percentage points on the $\leq$ 1 pixel error, highlighting
its potential for improving accuracy in this task. The code of VisiTherS is
available on GitHub at the following link
https://github.com/philippeDG/VisiTherS.
