Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

We present a novel holistic deep-learning approach for multi-task learning from a single
indoor panoramic image. Our framework named MultiPanoWise extends vision transformers …

We present a learning-based approach to relight a single image of Lambertian and low-
frequency specular objects. Our method enables inserting objects from photographs into …

We present a method to reconstruct indoor and outdoor static scene geometry and
appearance from an omnidirectional video moving in a small circular sweep. This setting is …

We present a efficient multi-view inverse rendering method for large-scale real-world indoor
scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike …

Intrinsic image decomposition aims to separate the surface reflectance and the effects from
the illumination given a single photograph. Due to the complexity of the problem, most prior …

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-
varying reflectance, homogeneous subsurface scattering parameters, and an environment …

Multi-view inverse rendering is the problem of estimating the scene parameters such as
shapes materials or illuminations from a sequence of images captured under different …

In this paper, we propose a novel method for joint recovery of camera pose, object geometry
and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes …

Abstract Today's Extended Reality (XR) applications that call for specific Diminished Reality
(DR) strategies to hide specific classes of objects are increasingly using 360° cameras …