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 …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Recent advancements in 2D diffusion models allow appearance generation on untextured
raw meshes. These methods create RGB textures by distilling a 2D diffusion model, which …

This paper aims to recover object materials from posed images captured under an unknown
static lighting condition. Recent methods solve this task by optimizing material parameters …

State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene
representations, which require sampling multiple points to compute the color arriving along …

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

Relighting radiance fields is severely underconstrained for multi‐view data, which is most
often captured under a single illumination condition; It is especially hard for full scenes …

This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene
factorization. Compared to existing methods leveraging discrete meshes or neural implicit …

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 …

Learning from multi-view images using neural implicit signed distance functions shows
impressive performance on 3D Reconstruction of opaque objects. However, existing …