Reconstruction and intrinsic decomposition of scenes from captured imagery would enable
many applications such as relighting and virtual object insertion. Recent NeRF based …

Performance is a critical challenge in mobile image processing. Given a reference imaging
pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements …

Intrinsic image decomposition separates an image into a reflectance layer and a shading
layer. Automatic intrinsic image decomposition remains a significant challenge, particularly …

This paper addresses extracting two layers from an image where one layer is smoother than
the other. This problem arises most notably in intrinsic image decomposition and reflection …

In this work, we address the problem of jointly estimating albedo, normals, depth and 3D
spatially-varying lighting from a single image. Most existing methods formulate the task as …

Shadow detection and shadow removal are fundamental and challenging tasks, requiring
an understanding of the global image semantics. This paper presents a novel deep neural …

We present a framework to automatically detect and remove shadows in real world scenes
from a single image. Previous works on shadow detection put a lot of effort in designing …

Identifying sparse salient structures from dense pixels is a longstanding problem in visual
computing. Solutions to this problem can benefit both image manipulation and …

We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo,
shading, and specular highlights from a single image of an object. Based on existing 3D …

We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a
single RGB-D image and estimates albedo and shading fields that explain the input. To …