274 citations · 710 across the 42 of their papers we have counts for
43 papers
Neural BRDF Importance Sampling by Reparameterization
Liwen Wu, Sai Bi, Zexiang Xu +5
Neural bidirectional reflectance distribution functions (BRDFs) have emerged as popular material representations for enhancing realism in physically-based rendering. Yet their impo…
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
Tianyuan Zhang, Zhengfei Kuang, Haian Jin +9
We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) pos…
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting
Guangyan Cai, Fujun Luan, Miloš Hašan +5
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. In this paper, we introduce PBIR-NIE, an inverse rendering…
Neural Gaffer: Relighting Any Object via Diffusion
Haian Jin, Yuan Li, Fujun Luan +6
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only…
Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
Liwen Wu, Sai Bi, Zexiang Xu +5
Novel-view synthesis of specular objects like shiny metals or glossy paints remains a significant challenge. Not only the glossy appearance but also global illumination effects, in…
GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting
Kai Zhang, Sai Bi, Hao Tan +4
We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our m…
MeshLRM: Large Reconstruction Model for High-Quality Meshes
Xinyue Wei, Kai Zhang, Sai Bi +6
We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconst…
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
Peng Wang, Hao Tan, Sai Bi +6
We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating…
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model
Jiahao Li, Hao Tan, Kai Zhang +7
Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from…
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
Yinghao Xu, Hao Tan, Fujun Luan +8
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model inco…
Controllable Dynamic Appearance for Neural 3D Portraits
ShahRukh Athar, Zhixin Shu, Zexiang Xu +4
Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and vie…
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
Isabella Liu, Linghao Chen, Ziyang Fu +8
We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different…
Strivec: Sparse Tri-Vector Radiance Fields
Quankai Gao, Qiangeng Xu, Hao Su +2
We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our appr…
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
Nithin Raghavan, Yan Xiao, Kai-En Lin +5
Precomputed Radiance Transfer (PRT) remains an attractive solution for real-time rendering of complex light transport effects such as glossy global illumination. After precomputati…
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
Minghua Liu, Chao Xu, Haian Jin +4
Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a…
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
Xinyue Wei, Fanbo Xiang, Sai Bi +4
We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend t…
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
Kaizhi Yang, Xiaoshuai Zhang, Zhiao Huang +3
We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on…
TensoIR: Tensorial Inverse Rendering
Haian Jin, Isabella Liu, Peijia Xu +6
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus sufferin…
Factor Fields: A Unified Framework for Neural Fields and Beyond
Anpei Chen, Zexiang Xu, Xinyue Wei +3
We present Factor Fields, a novel framework for modeling and representing signals. Factor Fields decomposes a signal into a product of factors, each represented by a classical or n…
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy +5
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input im…
RigNeRF: Fully Controllable Neural 3D Portraits
ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli +2
Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not suppor…
ARF: Artistic Radiance Fields
Kai Zhang, Nick Kolkin, Sai Bi +4
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene. Previous methods that perform 3D stylization on point clouds or meshes are sen…
Differentiable Rendering of Neural SDFs through Reparameterization
Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li +7
We present a method to automatically compute correct gradients with respect to geometric scene parameters in neural SDF renderers. Recent physically-based differentiable rendering…