Because of the diversity in lighting environments, existing illumination estimation techniques
have been designed explicitly on indoor or outdoor environments. Methods have focused …

In this work, we present I^ 2-SDF, a new method for intrinsic indoor scene reconstruction and
editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs) …

Abstract Physically-Based Rendering (PBR) is key to modeling the interaction between light
and materials and finds extensive applications across computer graphics domains. However …

With the popularity of implicit neural representations, or neural radiance fields (NeRF), there
is a pressing need for editing methods to interact with the implicit 3D models for tasks like …

We describe a novel method StyLitGAN for relighting and resurfacing images in the absence
of labeled data. StyLitGAN generates images with realistic lighting effects including cast …

Inverse path tracing has recently been applied to joint material and lighting estimation, given
geometry and multi-view HDR observations of an indoor scene. However, it has two major …

Neural radiance fields have achieved remarkable performance in modeling the appearance
of 3D scenes. However existing approaches still struggle with the view-dependent …

In this paper, we study the problem of semi-supervised 3D object detection, which is of great
importance considering the high annotation cost for cluttered 3D indoor scenes. We resort to …

Physical simulations produce excellent predictions of weather effects. Neural radiance fields
produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse …

We propose a physically motivated deep learning framework to solve a general version of
the challenging indoor lighting estimation problem. Given a single LDR image with a depth …