16 citations · 61 across the 10 of their papers we have counts for
10 papers
Spatiotemporally Consistent HDR Indoor Lighting Estimation
Zhengqin Li, Li Yu, Mikhail Okunev +2
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 ma…
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…
Learning to Rearrange with Physics-Inspired Risk Awareness
Meng Song, Yuhan Liu, Zhengqin Li +1
Real-world applications require a robot operating in the physical world with awareness of potential risks besides accomplishing the task. A large part of risky behaviors arises fro…
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes
Rui Zhu, Zhengqin Li, Janarbek Matai +2
Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Sh…
Physically-Based Editing of Indoor Scene Lighting from a Single Image
Zhengqin Li, Jia Shi, Sai Bi +6
We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that…
Learning Neural Transmittance for Efficient Rendering of Reflectance Fields
Mohammad Shafiei, Sai Bi, Zhengqin Li +3
Recently neural volumetric representations such as neural reflectance fields have been widely applied to faithfully reproduce the appearance of real-world objects and scenes under…
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Zhengqin Li, Ting-Wei Yu, Shen Sang +14
We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make th…
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflectio…
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi +2
We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we create a complete scene reconstruction, estimating shape,…
Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image
Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker
We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone cam…