Bonjour à tous et à toutes,

Vous êtes cordialement invité.e.s à la soutenance de thèse de Pierre-André Brousseau, le jeudi 18 septembre, à 14:00pm

Titre: Stereoscopic Depth Estimation with Permutation

Date: jeudi le 18 september 2025 à 14h

Location: Pavillon André-Aisenstadt, salle 3195, 2920 Ch. de la tour

Jury

Abstract:

This thesis is in the field of depth estimation in computer vision. Its specific interests are stereoscopic depth estimation, deep stereo matching, traditional stereo matching and monocular depth estimation. Its main contribution is a permutation formulation for stereo matching which is well suited for self-supervised stereo matching, where stereo neural networks are trained without ground truth disparity maps. The permutation formulation is further demonstrated to allow the training of a feature encoder which can be introduced in traditional stereo matching algorithms and that can be readily integrated in industry level vision systems. Finally, this thesis will extend the permutation model to single camera systems by introducing spherical rectification, a novel epipolar rectification method for generic motion. These contributions represent actionable solution paths for problems faced in the industry. Deep stereo matching has not been adopted in the industry because traditional algorithms have kept improving and remain highly generalizable, reliable and explainable regardless of benchmark performance. We expect neural networks to fill the role of depth completion and depth denoising as they can introduce single image information while also allowing for better feature representations. Monocular depth algorithms which cannot be solved in a traditional manner have emerged by relying on a large transformer based model but remain reliable only for relative depth. This will change in the coming years as physical constraints are introduced such as the monocular stereo presented in this thesis.