My name is Chenyang Zhu. I am currently an Assistant Professor at School of Computer Science, National University of Defense Technology (NUDT).

I am a faculty member of iGraphics Lab @ NUDT, which conducts research in the areas of computer graphics and computer vision. The current directions of interest include data-driven shape analysis and modeling, 3D vision and robot perception & navigation, etc.

I was a Ph.D. student in Gruvi Lab, school of Computing Science at Simon Fraser University, under the supervision of Prof. Hao(Richard) Zhang. I earned my Bachelor and Master degree in computer science from National University of Defense Technology (NUDT) in Jun. 2011 and Dec. 2013 respectively. My research interest is computer graphics with a focus on geometry processing, shape analysis and deformation.

Recent Publications

Online 3D Bin Packing with Constrained Deep Reinforcement Learning

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang and Kai Xu ,"Online 3D Bin Packing with Constrained Deep Reinforcement Learning", arXiv:2006.14978

We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP). In our problem, the agent has limited information about the items to be packed into the bin, and an item must be packed immediately after its arrival without buffering or readjusting. The item's placement also subjects to the constraints of collision avoidance and physical stability.

arxiv

Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation

Jiazhao Zhang*, Chenyang Zhu*, Lintao Zheng and Kai Xu ,"Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation", CVPR 2020. *joint first author

Online semantic 3D segmentation in company with realtime RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data, and how to smartly fuse information from frame to frame.

arxiv

AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss

Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas J. Guibas and Hao Zhang , "AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss", CVPR 2020, Oral presentation.

We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set.

arxiv

Active Scene Understanding via Online Semantic Reconstruction

Lintao Zheng, Chenyang Zhu, Jiazhao Zhang, Hang Zhao, Hui Huang, Matthias Niessner and Kai Xu , "Active Scene Understanding via Online Semantic Reconstruction", Pacific Graphics 2019.

We propose a novel approach to robot-operated active understanding of unknown indoor scenes, based on online RGBD reconstruction with semantic segmentation. In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene. Our algorithm is built on top of the volumetric depth fusion framework (e.g., KinectFusion) and performs real-time voxel-based semantic labeling over the online reconstructed volume. The robot is guided by an online estimated discrete viewing score field (VSF) parameterized over the 3D space of ...

arxiv

PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation

Fenggen Yu, Kun Liu, Yan Zhang, Chenyang Zhu and Kai Xu , "PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation", CVPR 2019.

Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for topdown recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the hierarchy share weights. At each node, a node classifier is trained to determine the type (adjacency or symmetry) and stopping criteria of its decomposition ...

arxiv

SCORES: Shape Composition with Recursive Substructure Priors

Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi and Hao Zhang , "SCORES: Shape Composition with Recursive Substructure Priors", SIGGRAPH ASIA 2018.

We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction....

arxiv

Interaction Context (ICON): Towards a Geometric Functionality Descriptor

Renjiao Yi, Chenyang Zhu, Ping Tan, Stephen Lin, "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction", ECCV 2018.

We present a method for estimating detailed scene illumination using human faces in a single image. In contrast to previous works that estimate lighting in terms of low-order basis functions or distant point lights, our technique estimates illumination at a higher precision in the form of a non-parametric environment map...

arxiv

Interaction Context (ICON): Towards a Geometric Functionality Descriptor

Chenyang Zhu, Renjiao Yi, Wallace Lira, Ibraheem Alhashim, Kai Xuand Hao Zhang, "Deformation-Driven Shape Correspondence via Shape Recognition", ACM Transactions on Graphics (SIGGRAPH 2017), 36(4): 51, 2017.

Many approaches to shape comparison and recognition start by establishing a shape correspondence. We "turn the table" and show that quality shape correspondences can be obtained by performing many shape recognition tasks. What is more, the method we develop computes a fine-grained, topology-varying part correspondence between two 3D shapes where the core evaluation mechanism only recognizes shapes globally...

PDF

Demo

Interaction Context (ICON): Towards a Geometric Functionality Descriptor

Ruizhen Hu, Chenyang Zhu, Oliver van Kaick, Ligang Liu, Ariel Shamir and Hao Zhang, "Interaction Context (ICON): Towards a Geometric Functionality Descriptor", ACM Transactions on Graphics (SIGGRAPH 2015), 33(4): 83, 2015.

We introduce a contextual descriptor which aims to provide a geometric description of the functionality of a 3D object in the context of a given scene. Differently from previous works, we do not regard functionality as an abstract label or represent it implicitly through an agent. Our descriptor, called interaction context or ICON for short, explicitly represents the geometry of object-to-object interactions...

PDF

Organizing Heterogeneous Scene Collections through Contextual Focal Points

Kai Xu, Rui Ma, Hao Zhang, Chenyang Zhu, Ariel Shamir, Daniel Cohen-Or and Hui Huang, "Organizing Heterogeneous Scene Collections through Contextual Focal Points", ACM Transactions on Graphics (SIGGRAPH 2014), 33(4): 35, 2014.

We introduce focal points for characterizing, comparing, and organizing collections of complex and heterogeneous data and apply the concepts and algorithms developed to collections of 3D indoor scenes. We represent each scene by a graph of its constituent objects and define focal points as representative substructures in a scene collection. To organize a heterogeneous scene collection, we cluster the scenes...

PDF

Get In Touch

  • Address

    School Of Computing Science
    National University of Defense Technology
    109 Deya Rd.
    Kaifu District
    Changsha, Hunan. 410073
    China
  • Email

    chenyang.chandler.zhu@gmail.com
#
#
#