261 citations · 784 across the 21 of their papers we have counts for
20 papers · 1 filter
Recovering 3D Shapes from Ultra-Fast Motion-Blurred Images
Fei Yu, Shudan Guo, Shiqing Xin +3
We consider the problem of 3D shape recovery from ultra-fast motion-blurred images. While 3D reconstruction from static images has been extensively studied, recovering geometry fro…
Towards Viewpoint Robustness in Bird's Eye View Segmentation
Tzofi Klinghoffer, Jonah Philion, Wenzheng Chen +6
Autonomous vehicles (AV) require that neural networks used for perception be robust to different viewpoints if they are to be deployed across many types of vehicles without the rep…
Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
Binbin Huang, Xingyue Peng, Siyuan Shen +8
We introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-…
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes
Zian Wang, Tianchang Shen, Jun Gao +6
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods…
HumanGen: Generating Human Radiance Fields with Explicit Priors
Suyi Jiang, Haoran Jiang, Ziyu Wang +3
Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fie…
Relightable Neural Human Assets from Multi-view Gradient Illuminations
Taotao Zhou, Kai He, Di Wu +6
Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most exist…
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Jun Gao, Tianchang Shen, Zian Wang +6
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of…
Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion
Zian Wang, Wenzheng Chen, David Acuna +2
We consider the challenging problem of outdoor lighting estimation for the goal of photorealistic virtual object insertion into photographs. Existing works on outdoor lighting esti…
NARRATE: A Normal Assisted Free-View Portrait Stylizer
Youjia Wang, Teng Xu, Yiwen Wu +4
In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical fac…
Extracting Triangular 3D Models, Materials, and Lighting From Images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen +5
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, wh…
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen, Joey Litalien, Jun Gao +5
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers. Many previous learning-based approaches fo…
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Jun Gao, Wenzheng Chen, Tommy Xiang +4
3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction…
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Yuxuan Zhang, Wenzheng Chen, Huan Ling +4
Differentiable rendering has paved the way to training neural networks to perform "inverse graphics" tasks such as predicting 3D geometry from monocular photographs. To train high…
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche +6
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning…
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Jun Gao, Huan Ling +4
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enab…
Fast Interactive Object Annotation with Curve-GCN
Huan Ling, Jun Gao, Amlan Kar +2
Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent m…
Steady-state Non-Line-of-Sight Imaging
Wenzheng Chen, Simon Daneau, Fahim Mannan +1
Conventional intensity cameras recover objects in the direct line-of-sight of the camera, whereas occluded scene parts are considered lost in this process. Non-line-of-sight imagin…
Bundle Optimization for Multi-aspect Embedding
Qiong Zeng, Baoquan Chen, Yanir Kleiman +2
Understanding semantic similarity among images is the core of a wide range of computer vision applications. An important step towards this goal is to collect and learn human percep…
A Holistic Approach for Data-Driven Object Cutout
Huayong Xu, Yangyan Li, Wenzheng Chen +3
Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerab…
Synthesizing Training Images for Boosting Human 3D Pose Estimation
Wenzheng Chen, Huan Wang, Yangyan Li +6
Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on t…