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Marr revisited: 2D-3D alignment via surface normal prediction

Marr revisited: 2D-3D alignment via surface normal predictio...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Bansal, Aayush Russell, Bryan Gupta, Abhinav Carnegie Mellon University United States Adobe Research United States

ISBN: (纸本)9781467388511

We introduce an approach that leverages surface normal predictions, along with appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large CAD object library. Critical to the success of our approach is the ability to recover accurate surface normals for objects in the depicted scene. We introduce a skip-network model built on the pre-trained Oxford VGG convolutional neural network (CNN) for surface normal prediction. Our model achieves state-of-the-art accuracy on the NYUv2 RGB-D dataset for surface normal prediction, and recovers fine object detail compared to previous methods. Furthermore, we develop a two-stream network over the input image and predicted surface normals that jointly learns pose and style for CAD model retrieval. When using the predicted surface normals, our two-stream network matches prior work using surface normals computed from RGB-D images on the task of pose prediction, and achieves state of the art when using RGB-D input. Finally, our two-stream network allows us to retrieve CAD models that better match the style and pose of a depicted object compared with baseline approaches.

关键词： computer aided design

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Three-dimensional object detection and layout prediction using clouds of oriented gradients

Three-dimensional object detection and layout prediction usi...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ren, Zhile Sudderth, Erik B. Department of Computer Science Brown University ProvidenceRI02912 United States

ISBN: (纸本)9781467388511

We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes. RGB-D images are traditionally described by local geometric features of the 3D point cloud. We propose a cloud of oriented gradient (COG) descriptor that links the 2D appearance and 3D pose of object categories, and thus accurately models how perspective projection affects perceived image boundaries. We also propose a "Manhattan voxel" representation which better captures the 3D room layout geometry of common indoor environments. Effective classification rules are learned via a structured prediction framework that accounts for the intersection-over-union overlap of hypothesized 3D cuboids with human annotations, as well as orientation estimation errors. Contextual relationships among categories and layout are captured via a cascade of classifiers, leading to holistic scene hypotheses with improved accuracy. Our model is learned solely from annotated RGB-D images, without the benefit of CAD models, but nevertheless its performance substantially exceeds the state-of-the-art on the SUN RGB-D database. Avoiding CAD models allows easier learning of detectors for many object categories.

关键词： Object detection

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Single-image crowd counting via multi-column convolutional neural network

Single-image crowd counting via multi-column convolutional n...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Yingying Zhou, Desen Chen, Siqin Gao, Shenghua Ma, Yi Shanghaitech University China

ISBN: (纸本)9781467388511

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

关键词： computer vision

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Semantic object parsing with Local-Global Long Short-Term Memory

Semantic object parsing with Local-Global Long Short-Term Me...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Liang, Xiaodan Shen, Xiaohui Xiang, Donglai Feng, Jiashi Lin, Liang Yan, Shuicheng Sun Yat-sen University NUS Singapore 360 AI Institute China National University of Singapore Singapore Adobe Research United States

ISBN: (纸本)9781467388511

Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.

关键词： pattern recognition

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Joint learning of single-image and cross-image representations for person re-identification

Joint learning of single-image and cross-image representatio...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Faqiang Zuo, Wangmeng Lin, Liang Zhang, David Zhang, Lei School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Computing Hong Kong Polytechnic University Hong Kong Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781467388511

Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning framework to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.

关键词： Image processing

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A text detection system for natural scenes with convolutional feature learning and cascaded classification

A text detection system for natural scenes with convolutiona...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhu, Siyu Zanibbi, Richard Center for Imaging Science Rochester Institute of Technology NY United States Department of Computer Science Rochester Institute of Technology NY United States

ISBN: (纸本)9781467388511

We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character/word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes.

关键词： Feature extraction

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A hole filling approach based on background reconstruction for view synthesis in 3D video

A hole filling approach based on background reconstruction f...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Luo, Guibo Zhu, Yuesheng Li, Zhaotian Zhang, Liming Commun. Inf. Secur. Lab Shenzhen Graduate School Peking University China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781467388511

The depth image based rendering (DIBR) plays a key role in 3D video synthesis, by which other virtual views can be generated from a 2D video and its depth map. However, in the synthesis process, the background occluded by the foreground objects might be exposed in the new view, resulting in some holes in the synthetized video. In this paper, a hole filling approach based on background reconstruction is proposed, in which the temporal correlation information in both the 2D video and its corresponding depth map are exploited to construct a background video. To construct a clean background video, the foreground objects are detected and removed. Also motion compensation is applied to make the background reconstruction model suitable for moving camera scenario. Each frame is projected to the current plane where a modified Gaussian mixture model is performed. The constructed background video is used to eliminate the holes in the synthetized video. Our experimental results have indicated that the proposed approach has better quality of the synthetized 3D video compared with the other methods.

关键词： Three dimensional computer graphics

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iLab-20M: A large-scale controlled object dataset to investigate deep learning

iLab-20M: A large-scale controlled object dataset to investi...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Borji, Ali Izadi, Saeed Itti, Laurent Center for Research in Computer Vision University of Central Florida United States Amirkabir University of Technology Iran University of Southern California United States

ISBN: (纸本)9781467388511

Tolerance to image variations (e.g., translation, scale, pose, illumination, background) is an important desired property of any object recognition system, be it human or machine. Moving towards increasingly bigger datasets has been trending in computer vision especially with the emergence of highly popular deep learning models. While being very useful for learning invariance to object inter- and intra-class shape variability, these large-scale wild datasets are not very useful for learning invariance to other parameters urging researchers to resort to other tricks for training models. In this work, we introduce a large-scale synthetic dataset, which is freely and publicly available, and use it to answer several fundamental questions regarding selectivity and invariance properties of convolutional neural networks. Our dataset contains two parts: a) objects shot on a turntable: 15 categories, 8 rotation angles, 11 cameras on a semi-circular arch, 5 lighting conditions, 3 focus levels, variety of backgrounds (23.4 per instance) generating 1320 images per instance (about 22 million images in total), and b) scenes: in which a robotic arm takes pictures of objects on a 1:160 scale scene. We study: 1) invariance and selectivity of different CNN layers, 2) knowledge transfer from one object category to another, 3) systematic or random sampling of images to build a train set, 4) domain adaptation from synthetic to natural scenes, and 5) order of knowledge delivery to CNNs. We also discuss how our analyses can lead the field to develop more efficient deep learning methods.

关键词： Deep learning

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Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions

Self-adaptive matrix completion for heart rate estimation fr...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Tulyakov, Sergey Alameda-Pineda, Xavier Ricci, Elisa Yin, Lijun Cohn, Jeffrey F. Sebe, Nicu University of Trento Via Sommarive 9 Trento38123 Italy Fondazione Bruno Kessler Via Sommarive 18 Trento38123 Italy University of Perugia Via Duranti 93 Perugia06123 Italy State University of New York at Binghamton BinghamtonNY13902 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Pittsburgh PittsburghPA15260 United States

ISBN: (纸本)9781467388511

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, stateof-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-ofthe-art HR estimation methods in naturalistic conditions.

关键词： computer vision

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Multicamera calibration from visible and mirrored epipoles

Multicamera calibration from visible and mirrored epipoles

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Bushnevskiy, Andrey Sorgi, Lorenzo Rosenhahn, Bodo Technicolor Research and Innovation Karl-Wiechert Allee 74 Hannover30625 Germany Leibniz University Hannover Appelstr 9A Hannover30169 Germany

ISBN: (纸本)9781467388511

Multicamera rigs are used in a large number of 3D vision applications, such as 3D modeling, motion capture or telepresence and a robust calibration is of utmost importance in order to achieve a high accuracy results. In many practical configurations the cameras in a rig are arranged in such a way, that they can observe each other, in other words a number of epipoles correspond to the real image points. In this paper we propose a solution for the automatic recovery of the external calibration of a multicamera system by enforcing only simple geometrical constraints, arising from the epipole visibility, without using any calibration object, such as checkerboards, laser pointers or similar. Additionally, we introduce an extension of the method that handles the case of epipoles being visible in the reflection of a planar mirror, which makes the algorithm suitable for the calibration of any multicamera system, irrespective of the number of cameras and their actual mutual visibility, and furthermore we remark that it requires only one or a few images per camera and therefore features a high speed and usability. We produce an evidence of the algorithm effectiveness by presenting a wide set of tests performed on synthetic as well as real datasets and we compare the results with those obtained using a traditional LED-based algorithm. The real datasets have been captured using a multicamera Virtual Reality (VR) rig and a spherical dome configuration for 3D reconstruction.

关键词： Three dimensional computer graphics

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