Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

With the rapid development of 3D reconstruction, especially the emergence of algorithms
such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent …

In this paper, we present a novel differentiable point-based rendering framework to achieve
photo-realistic relighting. To make the reconstructed scene relightable, we enhance vanilla …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

We present an end-to-end inverse rendering pipeline that includes a fully differentiable
renderer, and can reconstruct geometry, materials, and illumination from scratch from a set …

Abstract We present Intrinsic Image Diffusion a generative model for appearance
decomposition of indoor scenes. Given a single input view we sample multiple possible …

Physically-based differentiable rendering has recently emerged as an attractive new
technique for solving inverse problems that recover complete 3D scene representations from …

Modern rendering systems are confronted with a dauntingly large and growing set of
requirements: in their pursuit of realism, physically based techniques must increasingly …

We introduce a differentiable rasterizer that bridges the vector graphics and raster image
domains, enabling powerful raster-based loss functions, optimization procedures, and …

We present a differentiable rendering framework for material and lighting estimation from
multi-view images and a reconstructed geometry. In the framework, we represent scene …