We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on
measurements by miniature energy-efficient low-cost single-photon cameras. These …

Unsigned distance functions (UDFs) have been a vital representation for open surfaces.
With different differentiable renderers, current methods are able to train neural networks to …

Neural approaches have shown a significant progress on camera-based reconstruction. But
they require either a fairly dense sampling of the viewing sphere or pre-training on an …

Multi-task approaches to joint depth and segmentation prediction are well-studied for
monocular images. Yet, predictions from a single-view are inherently limited, while multiple …

This paper introduces a versatile paradigm for integrating multi-view reflectance (optional)
and normal maps acquired through photometric stereo. Our approach employs a pixel-wise …

We present SuperNormal a fast high-fidelity approach to multi-view 3D reconstruction using
surface normal maps. With a few minutes SuperNormal produces detailed surfaces on par …

Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery
to obtain better coverage of the organ and detection of various health issues. Despite …

In this work we present a novel multi-view photometric stereo (MVPS) method. Like many
works in 3D reconstruction we are leveraging neural shape representations and learnt …

Multi-view photometric stereo (MVPS) recovers a high-fidelity 3D shape of a scene by
benefiting from both multi-view stereo and photometric stereo. While photometric stereo …

We present differentiable point-based inverse rendering DPIR an analysis-by-synthesis
method that processes images captured under diverse illuminations to estimate shape and …