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Juyong Zhang

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4 papers

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NeurIPS Conference 2022 Conference Paper

Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

Hongrui Cai
Wanquan Feng
Xuetao Feng
Yan Wang
Juyong Zhang

We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to jointly optimize the surface and deformations. To represent and constrain the non-rigid deformations, we propose a novel neural invertible deforming network such that the cycle consistency between arbitrary two frames is automatically satisfied. Considering that the surface topology of dynamic scene might change over time, we employ a topology-aware strategy to construct the topology-variant correspondence for the fused frames. NDR also further refines the camera poses in a global optimization manner. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstruction methods.

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AAAI Conference 2021 Conference Paper

Learning Local Neighboring Structure for Robust 3D Shape Representation

Zhongpai Gao
Junchi Yan
Guangtao Zhai
Juyong Zhang
Yiyan Yang
Xiaokang Yang

Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e. g. , images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D shape data are irregular since each node’s neighbors are unordered. Various graph neural networks for 3D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in the random synthesizer – a new Transformer model for natural language processing (NLP). Comprehensive experiments demonstrate that our model produces significant improvement in 3D shape reconstruction compared to state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

Diversified Interactive Recommendation with Implicit Feedback

Yong Liu
Yingtai Xiao
Qiong Wu
Chunyan Miao
Juyong Zhang
Binqiang Zhao
Haihong Tang

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversiﬁed recommendation model, named Diversiﬁed Contextual Combinatorial Bandit (DC2 B), for interactive recommendation with users’ implicit feedback. Speciﬁcally, DC2 B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC2 B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.

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IJCAI Conference 2019 Conference Paper

Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos

Haofei Xu
Jianmin Zheng
Jianfei Cai
Juyong Zhang

While learning based depth estimation from images/videos has achieved substantial progress, there still exist intrinsic limitations. Supervised methods are limited by a small amount of ground truth or labeled data and unsupervised methods for monocular videos are mostly based on the static scene assumption, not performing well on real world scenarios with the presence of dynamic objects. In this paper, we propose a new learning based method consisting of DepthNet, PoseNet and Region Deformer Networks (RDN) to estimate depth from unconstrained monocular videos without ground truth supervision. The core contribution lies in RDN for proper handling of rigid and non-rigid motions of various objects such as rigidly moving cars and deformable humans. In particular, a deformation based motion representation is proposed to model individual object motion on 2D images. This representation enables our method to be applicable to diverse unconstrained monocular videos. Our method can not only achieve the state-of-the-art results on standard benchmarks KITTI and Cityscapes, but also show promising results on a crowded pedestrian tracking dataset, which demonstrates the effectiveness of the deformation based motion representation. Code and trained models are available at https: //github. com/haofeixu/rdn4depth.

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