Search Results for author: yinda zhang

Found 33 papers, 17 papers with code

Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations ECCV 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

Multiresolution Deep Implicit Functions for 3D Shape Representation

no code implementations ICCV 2021 Zhang Chen, yinda zhang, Kyle Genova, Sean Fanello, Sofien Bouaziz, Christian Haene, Ruofei Du, Cem Keskin, Thomas Funkhouser, Danhang Tang

To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.

3D Reconstruction 3D Shape Representation

Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering

no code implementations ICCV 2021 Bangbang Yang, yinda zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, Zhaopeng Cui

In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene.

Neural Rendering Novel View Synthesis

DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization

1 code implementation ICCV 2021 Cheng Zhang, Zhaopeng Cui, Cai Chen, Shuaicheng Liu, Bing Zeng, Hujun Bao, yinda zhang

Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods.

Scene Understanding

Deep Hybrid Self-Prior for Full 3D Mesh Generation

no code implementations ICCV 2021 Xingkui Wei, Zhengqing Chen, Yanwei Fu, Zhaopeng Cui, yinda zhang

We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cloud.

Surface Reconstruction

Spatially-Varying Outdoor Lighting Estimation from Intrinsics

no code implementations CVPR 2021 Yongjie Zhu, yinda zhang, Si Li, Boxin Shi

We train a deep neural network to regress intrinsic cues with physically-based constraints and use them to conduct global and local lightings estimation.

HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences

1 code implementation CVPR 2021 Feitong Tan, Danhang Tang, Mingsong Dou, Kaiwen Guo, Rohit Pandey, Cem Keskin, Ruofei Du, Deqing Sun, Sofien Bouaziz, Sean Fanello, Ping Tan, yinda zhang

In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.

Learning Compositional Representation for 4D Captures with Neural ODE

no code implementations CVPR 2021 Boyan Jiang, yinda zhang, Xingkui Wei, xiangyang xue, Yanwei Fu

To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp.

Holistic 3D Scene Understanding from a Single Image with Implicit Representation

1 code implementation CVPR 2021 Cheng Zhang, Zhaopeng Cui, yinda zhang, Bing Zeng, Marc Pollefeys, Shuaicheng Liu

We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.

3D Object Detection Scene Understanding

Interacting Two-Hand 3D Pose and Shape Reconstruction From Single Color Image

no code implementations ICCV 2021 Baowen Zhang, Yangang Wang, Xiaoming Deng, yinda zhang, Ping Tan, Cuixia Ma, Hongan Wang

In this paper, we propose a novel deep learning framework to reconstruct 3D hand poses and shapes of two interacting hands from a single color image.

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

6 code implementations CVPR 2021 Vladimir Tankovich, Christian Häne, yinda zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz

Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.

Stereo Depth Estimation Stereo Disparity Estimation +1

Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations31 Mar 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

DeepSFM: Structure From Motion Via Deep Bundle Adjustment

1 code implementation ECCV 2020 Xingkui Wei, yinda zhang, Zhuwen Li, Yanwei Fu, xiangyang xue

The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology.

Pose Estimation Structure from Motion

Neural Point Cloud Rendering via Multi-Plane Projection

1 code implementation CVPR 2020 Peng Dai, yinda zhang, Zhuwen Li, Shuaicheng Liu, Bing Zeng

The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.

DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing

1 code implementation CVPR 2020 Shaohui Liu, yinda zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui

We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.

Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation

1 code implementation ICCV 2019 Chao Wen, yinda zhang, Zhuwen Li, Yanwei Fu

We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses.

Multi-level Semantic Feature Augmentation for One-shot Learning

1 code implementation15 Apr 2018 Zitian Chen, Yanwei Fu, yinda zhang, Yu-Gang Jiang, xiangyang xue, Leonid Sigal

In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet.

One-Shot Learning

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

3 code implementations ECCV 2018 Nanyang Wang, yinda zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang

We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.

3D Object Reconstruction

Deep Depth Completion of a Single RGB-D Image

1 code implementation CVPR 2018 Yinda Zhang, Thomas Funkhouser

The goal of our work is to complete the depth channel of an RGB-D image.

Depth Completion Depth Estimation

VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING

no code implementations ICLR 2018 jianqi ma, Hangyu Lin, yinda zhang, Yanwei Fu, xiangyang xue

Besides directly augmenting image features, we transform the image features to semantic space using the encoder and perform the data augmentation.

Classification Data Augmentation +2

Hand3D: Hand Pose Estimation using 3D Neural Network

no code implementations7 Apr 2017 Xiaoming Deng, Shuo Yang, yinda zhang, Ping Tan, Liang Chang, Hongan Wang

We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image.

3D Hand Pose Estimation

Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

no code implementations CVPR 2017 Yinda Zhang, Shuran Song, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin, Thomas Funkhouser

One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection.

Boundary Detection Edge Detection +4

Joint Hand Detection and Rotation Estimation by Using CNN

no code implementations8 Dec 2016 Xiaoming Deng, Ye Yuan, Yinda Zhang, Ping Tan, Liang Chang, Shuo Yang, Hongan Wang

Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.

General Classification Hand Detection +1

DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

no code implementations ICCV 2017 Yinda Zhang, Mingru Bai, Pushmeet Kohli, Shahram Izadi, Jianxiong Xiao

In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models.

Scene Understanding

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

3 code implementations10 Jun 2015 Fisher Yu, Ari Seff, yinda zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry.

TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

1 code implementation25 Apr 2015 Pingmei Xu, Krista A. Ehinger, yinda zhang, Adam Finkelstein, Sanjeev R. Kulkarni, Jianxiong Xiao

Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.

Eye Tracking Saliency Prediction

FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps

no code implementations CVPR 2013 Yinda Zhang, Jianxiong Xiao, James Hays, Ping Tan

We analyze the self-similarity of the guide image to generate a set of allowable local transformations and apply them to the input image.

Image Generation

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