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24th ACM multimedia Conference, MM 2016

作者： Wang, Meng Fang, Yi NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates Dept. of CSE NYU Tandon School of Engineering United States

ISBN: (纸本)9781450336031

Identifying accurate correspondences information among different shapes is of great importance in shape analysis such as shape registration, segmentation and retrieval. This paper aims to develop a paradigm to address the challenging issues posed by shape structural variation and symmetry ambiguity. Specifically, the proposed research developed a novel shape signature based on local diffusion map on 3D surface, which is used to identify the shape correspondence through graph matching process. The developed shape signature, named local diffusion map signature (LDMS), is obtained by projecting heat diffusion distribution on 3D surface into 2D images along the surface normal direction with orientation determined by gradients of heat diffusion field. The local diffusion map signature is able to capture the concise geometric essence that is deformation-insensitive and symmetry-aware. Experimental results on 3D shape correspondence demonstrate the superior performance of our proposed method over other state-of-the-art techniques in identifying correspondences for non-rigid shapes with symmetry ambiguity. © 2016 ACM.

关键词： Diffusion

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Global consistent shape correspondence for efficient and effective active shape models 16

Global consistent shape correspondence for efficient and eff...

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24th ACM multimedia Conference, MM 2016

作者： Wang, Meng Fang, Yi NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates Dept. of ECE NYU Tandon School of Engineering United States

ISBN: (纸本)9781450336031

Finding the accurate corresponded landmarks from a collection of shape instances plays critical role in constructing active shape models (ASMs). We have developed a global consistent shape correspondence paradigm for efficient and effective active shape models to address challenging issues in statistical shape modelling. Specifically, in this paper, we developed techniques to perform a fast multiple shape matching to identify global consistent shape correspondence from a set of training shape instances via efficient low-rank recovery optimization. High quality ASMs can then be constructed based on the identified corresponded points. The entire process is unsupervised without manual annotation as well as free of selection of anatomically significant point. Experimental results on mobile hand image data demonstrate the superior performance of our proposed method over other state-of-the-art techniques like MDL in constructing active shape models. © 2016 ACM.

关键词： 3d shape correspondence non-rigid shape matching 3d shape signature

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ROSS: Robust Learning of One-Shot 3D Shape Segmentation

ROSS: Robust Learning of One-Shot 3D Shape Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shuaihang Yuan Yi Fang NYU Multimedia and Visual Computing Lab USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

3D shape segmentation is a fundamental computer vision task that partitions the object into labeled semantic parts. Recent approaches to 3D shape segmentation learning heavily rely on high-quality labeled training datasets. This limits their use in applications to handle the large scale unannotated datasets. In this paper, we proposed a novel semi-supervised approach, named Robust Learning of OneShot 3D Shape Segmentation (ROSS), which only requires one single exemplar labeled shape for training. The proposed ROSS can generalize its ability from a one-shot training process to predict the segmentation for previously unseen 3D shape models. The proposed ROSS is composed of three major modules for 3D shape segmentation as follows. The global shape descriptor generator is the first module that utilizes the proposed reference weighted convolution to learn a 3D shape descriptor. The second module is a part-aware shape descriptor constructor that can generate weighted descriptors from a learned 3D shape descriptor according to semantic parts without supervision. The shape morphing with label transferring works as the last module. It morphs the exemplar shape and then transfers labels from the transformed exemplar shape to the target shape. The extensive experimental results on 3D mesh datasets demonstrate the ROSS is robust to noise and incomplete shapes and it can be applied to unannotated datasets. The experiment shows the proposed ROSS can achieve comparable performance with the supervised method.

关键词： Shape Three-dimensional displays Task analysis Convolution Learning systems Robustness Training

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MDA-Net: Memorable Domain Adaptation Network for Monocular Depth Estimation 31

MDA-Net: Memorable Domain Adaptation Network for Monocular D...

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31st British Machine Vision Conference, BMVC 2020

作者： Zhu, Jing Shi, Yunxiao Ren, Mengwei Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Monocular depth estimation is a challenging task that aims to predict a corresponding depth map from a given single RGB image. Recent deep learning models have been proposed to predict the depth from the image by learning the alignment of deep features between the RGB image and the depth domains. In this paper, we present a novel approach, named Memorable Domain Adaptation Network (MDA-Net), to more effectively transfer domain features for monocular depth estimation by taking into account the common structure regularities (e.g., repetitive structure patterns, planar surfaces, symmetries) in domain adaptation. To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain. More specifically, in the SOM module, we develop a Memorable Bank of Filters (MBF) unit to learn a set of filters that memorize the structure-aware image-depth residual pattern, and also an Attention Guided Controller (AGC) unit to control the filter selection in the MBF given image features queries. Given the query image feature, the trained SOM module is able to adaptively select the best customized filters for cross-domain feature transferring with an optimal structural disparity between image and depth. In summary, we focus on addressing this structure-specific domain adaption challenge by proposing a novel end-to-end multi-scale memorable network for monocular depth estimation. The experiments show that our MDA-Net demonstrates the superior performance compared to the existing supervised monocular depth estimation approaches on the challenging KITTI and nyu Depth V2 benchmarks. © 2020. The copyright of this document resides with its authors.

关键词： Benchmarking

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Robust Image Matching By Dynamic Feature Selection 31

Robust Image Matching By Dynamic Feature Selection

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31st British Machine Vision Conference, BMVC 2020

作者： Huang, Hao Chen, Jianchun Li, Xiang Wang, Linging Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Estimating dense correspondences between images is a long-standing image understanding task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching. However, high-level feature maps are in low spatial resolution and therefore insufficient to provide accurate and fine-grained features to distinguish intra-class variations for correspondence matching. To address this problem, we generate robust features by dynamically selecting features at different scales. To resolve two critical issues in feature selection, i.e., how many and which scales of features to be selected, we frame the feature selection process as a sequential Markov decision-making process (MDP) and introduce an optimal selection strategy using reinforcement learning (RL). We define an RL environment for image matching in which each individual action either requires new features or terminates the selection episode by referring a matching score. Deep neural networks are incorporated into our method and trained for decision making. Experimental results show that our method achieves comparable/superior performance with state-of-the-art methods on three benchmarks, demonstrating the effectiveness of our feature selection strategy. © 2020. The copyright of this document resides with its authors.

关键词： Reinforcement learning

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ADAPTIVE WAVELET TRANSFORMER NETWORK FOR 3D SHAPE REPRESENTATION LEARNING 10

ADAPTIVE WAVELET TRANSFORMER NETWORK FOR 3D SHAPE REPRESENTA...

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10th International Conference on Learning Representations, ICLR 2022

作者： Huang, Hao Fang, Yi NYU Multimedia and Visual Computing Lab United States Abu Dhabi United Arab Emirates NYU Tandon School of Engineering New York University United States New York University Abu Dhabi United Arab Emirates

We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Previous works often decompose 3D shapes into complementary components in spatial domain at a single scale. In this work, we study to decompose 3D shapes into sub-bands components in frequency domain at multiple scales, resulting in a hierarchical decomposition tree in a principled manner rooted in multi-resolution wavelet analysis. Specifically, we propose Adaptive Wavelet Transformer Network (AWT-Net) that firstly generates approximation or detail wavelet coefficients per point, classifying each point into high or low sub-bands components, using lifting scheme at multiple scales recursively and hierarchically. Then, AWT-Net exploits Transformer to enhance the original shape features by querying and fusing features from different but integrated sub-bands. The wavelet coefficients can be learned without direct supervision on coefficients, and AWT-Net is fully differentiable and can be learned in an end-to-end fashion. Extensive experiments demonstrate that AWT-Net achieves competitive performance on 3D shape classification and segmentation benchmarks. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Wavelet decomposition

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Geometry-Aware Segmentation of Remote Sensing Images via implicit height estimation

arXiv

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arXiv 2020年

作者： Li, Xiang Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon Abu DhabiUAE United States

Deep learning-based methods, especially deep convolutional neural networks (CNNs), have made significant breakthroughs in the field of remote sensing and greatly advanced the performance of the semantic segmentation of remote sensing images. Recent studies have shown the benefits of using additional elevation data (e.g., DSM or nDSM) for enhancing the performance of the semantic segmentation of aerial images. However, previous methods mostly adopt 3D elevation information as additional inputs. While in many real-world applications, one does not have the corresponding DSM information at hand and the spatial resolution of acquired DSM images usually do not match the aerial images. To alleviate this data constraint and also take the advantage of 3D elevation information, in this paper, we propose a geometry-aware segmentation model that achieves accurate semantic segmentation of aerial images via implicit height estimation. Instead of using a single-stream encoder-decoder network for semantic labeling, we design a separate decoder branch to predict the height map and use the DSM images as side supervision to train this newly designed decoder branch. In this way, our model does not require DSM as model input and still benefits from the helpful 3D geometric information during training. With the newly designed decoder branch, our model can distill the 3D geometric features from 2D appearance features under the supervision of ground truth DSM images. Moreover, we develop a new geometry-aware convolution module that fuses the 3D geometric features from the height decoder branch and the 2D contextual features from the semantic segmentation branch. The fused feature embeddings can produce geometry-aware segmentation maps with enhanced performance. Our model is trained with DSM images as side supervision, while in the inference stage, it does not require DSM data and can directly predict the semantic labels in an end-to-end fashion. Experiments on ISPRS Vaihingen and Potsdam d

关键词： Convolution

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Unsupervised 3D reconstruction from a single image via adversarial learning

arXiv

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arXiv 2017年

作者： Wang, Lingjing Fang, Yi NYU Multimedia and Visual Computing Lab Courant Institute of Mathematical Science NYU Tandon School of Engineering United States NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates

Recent advancements in deep learning opened new opportunities for learning a high-quality 3D model from a single 2D image given sufficient training on large-scale data sets. However, the significant imbalance between available amount of images and 3D models, and the limited availability of labeled 2D image data (i.e. manually annotated pairs between images and their corresponding 3D models), severely impacts the training of most supervised deep learning methods in practice. In this paper, driven by a novel design of adversarial networks, we have developed an unsupervised learning paradigm to reconstruct 3D models from a single 2D image, which is free of manually annotated pairwise input image and its associated 3D model. Particularly, the paradigm begins with training an adaption network via autoencoder with adversarial loss, which embeds unpaired 2D synthesized image domain with real world image domain to a shared latent vector space. Then, we jointly train a 3D deconvolutional network to transform the latent vector space to the 3D object space together with the embedding process. Our experiments verify our network's robust and superior performance in handling 3D volumetric object generation from a single 2D image. Copyright © 2017, The Authors. All rights reserved.

关键词： Vector spaces

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An assistive low-vision platform that augments spatial cognition through proprioceptive guidance: Point-to-Tell-and-Touch

An assistive low-vision platform that augments spatial cogni...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wenjun Gui Bingyu Li Shuaihang Yuan John-Ross Rizzo Lakshay Sharma Chen Feng Anthony Tzes Yi Fang NYU Multimedia and Visual Computing Lab NYU Tandon Langone Medical Center NYU NYU Abu Dhabi NYU Tandon

Spatial cognition, as gained through the sense of vision, is one of the most important capabilities of human beings. However, for the visually impaired (VI), lack of this perceptual capability poses great challenges in their life. Therefore, we have designed Point-to-Tell-and-Touch, a wearable system with an ergonomic human-machine interface, for assisting the VI with active environmental exploration, with a particular focus on spatial intelligence and navigation to objects of interest in an alien environment. Our key idea is to link visual signals, as decoded synthetically, to the VI's proprioception for more intelligible guidance, in addition to vision-to-audio assistance, i.e., finger pose, as indicated by pointing, is used as “proprioceptive laser pointer” to target an object in that line of sight. The whole system consists of two features, Point-to-Tell and Point-to-Touch, both of which can work independently or cooperatively. The Point-to-Tell feature contains a camera with a novel one-stage neural network tailored for blind-centered object detection and recognition, and a headphone telling the VI the semantic label and distance from the pointed object. the Point-to-Touch, the second feature, leverages a vibrating wrist band to create a haptic feedback tool that supplements the initial vectorial guidance provided by the first stage (hand pose being direction and the distance being the extent, offered through audio cues). Both platform features utilize proprioception or joint position sense. Through hand pose, the VI end user knows where he or she is pointing relative to their egocentric coordinate system and we are able to use this foundation to build spatial intelligence. Our successful indoor experiments demonstrate the proposed system to be effective and reliable in helping the VI gain spatial cognition and explore the world in a more intuitive way.

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3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation

3D-MetaConNet: Meta-learning for 3D Shape Classification and...

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International Conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Hao Huang Xiang Li Lingjing Wang Yi Fang NYU Multimedia and Visual Computing Lab USA New York University Abu Dhabi UAE

ISBN: (纸本)9781665426893

Supervised learning on 3D shapes are extensively studied by prior literature, among which PointNet [29] and its variants PointNet++ [31] are representatives. However, these methods tackle 3D shape learning problems by training from scratch using a fixed learning algorithm over large amounts of labeled data, potentially challenged by data and computation bottlenecks. In the paper, we design a novel model, under the framework of meta-learning, to learn 3D shape representation. By training over multiple 3D tasks, each of which is defined as a supervised learning problem, our method can fast adapt to unseen tasks containing limited labeled data. Specifically, our model consists of a 3Dmeta-learner and a task-oriented 3D-learner, where the 3D-meta-learner produces parameter initialization for the 3D-learner after being trained over different tasks. With adaptively initialized parameters, the 3D-learner can be tuned rapidly in a few steps to achieve good performance on novel tasks with a small amount of training data. To further facilitate discriminative shape feature learning, we introduce a novel task-aware feature adaptation module under a contrastive learning scheme, in which all shapes in each task are considered as a whole and task-oriented compact features are learned. Therefore, we dub our model as 3DMetaConNet. Experiments on three public 3D datasets for few-shot shape classification and segmentation demonstrate that our method can learn compact and discriminative 3D shape features efficiently and robustly in a fast adaptation manner. Our method particularly outperforms the methods without a meta-learning framework and is also superior to existing meta-learning approaches.

关键词： Training Representation learning Adaptation models Solid modeling Three-dimensional displays Shape Computational modeling

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