Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach,
which can be trained end-to-end in an unsupervised manner from 2D point tracks …

This paper presents an uncalibrated deep neural network framework for the photometric
stereo problem. For training models to solve the problem, existing neural network-based …

Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D
navigation of surgical sites. However, most of the existing methods ignore the important …

We present a method for 3D face reconstruction from multi-view images with different
expressions. We formulate this problem from the perspective of non-rigid multi-view stereo …

We present a modern solution to the multi-view photometric stereo problem (MVPS). Our
work suitably exploits the image formation model in a MVPS experimental setup to recover …

Neural priors are a promising direction to capture low-level vision statistics without relying
on handcrafted regularizers. Recent works have successfully shown the use of neural …

This paper advocates the use of organic priors in classical non-rigid structure from motion
(NRS f M). By organic priors, we mean invaluable intermediate prior information intrinsic to …

We introduce an approach to enhance the novel view synthesis from images taken from a
freely moving camera. The introduced approach focuses on outdoor scenes where …

We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion
(NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also …