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MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

MAIR: Multi-view Attention Inverse Rendering with 3D Spatial...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Choi, JunYong Lee, SeokYeong Park, Haesol Jung, Seung-Won Kim, Ig-Jae Cho, Junghyun Korea Inst Sci & Technol KIST Seoul South Korea Korea Univ Seoul South Korea Univ Sci & Technol KIST Sch AI Robot Koto City Japan Yonsei Univ Yonsei KIST Convergence Res Inst Seoul South Korea

ISBN: (纸本)9798350301298

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.

关键词： vision + graphics

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GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

GPV-Pose: Category-level Object Pose Estimation via Geometry...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Di, Yan Zhang, Ruida Lou, Zhiqiang Manhardt, Fabian Ji, Xiangyang Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Tsinghua Univ Beijing Peoples R China Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. To circumvent this problem, category-level object pose estimation has recently been revamped, which aims at predicting the 6D pose as well as the 3D metric size for previously unseen instances from a given set of object classes. This is, however, a much more challenging task due to severe intra-class shape variations. To address this issue, we propose GPV-Pose, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category-level pose-sensitive features. First, we introduce a decoupled confidence-driven rotation representation, which allows geometry-aware recovery of the associated rotation matrix. Second, we propose a novel geometry-guided point-wise voting paradigm for robust retrieval of the 3D object bounding box. Finally, leveraging these different output streams, we can enforce several geometric consistency terms, further increasing performance, especially for non-symmetric categories. GPV-Pose produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS.

关键词： Point cloud compression Measurement computer vision Three-dimensional displays Shape Pose estimation Real-time systems

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Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection

Democracy Does Matter: Comprehensive Feature Mining for Co-S...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yu, Siyue Xiao, Jimin Zhang, Bingfeng Lim, Eng Gee XJTLU Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we aim to mine comprehensive co-salient features with democracy and reduce background interference without introducing any extra information. To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects. Then a comprehensive prototype based on the response maps can be generated as a guide for final prediction. To suppress the noisy background information in the prototype, we propose a self-contrastive learning module, where both positive and negative pairs are formed without relying on additional classification information. Besides, we also design a democratic feature enhancement module to further strengthen the co-salient features by readjusting attention values. Extensive experiments show that our model obtains better performance than previous state-of-the-art methods, especially on challenging real-world cases (e.g., for CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for maximum E-measure, and 3.7% for S-measure) under the same settings. Source code is available at https://***/siyueyu/DCFM.

关键词： computer vision Codes Aggregates Prototypes Object detection Interference Feature extraction

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Multi-Robot Active Mapping via Neural Bipartite Graph Matching

Multi-Robot Active Mapping via Neural Bipartite Graph Matchi...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ye, Kai Dong, Siyan Fan, Qingnan Wang, He Yi, Li Xia, Fei Wang, Jue Chen, Baoquan Peking Univ Beijing Peoples R China Shandong Univ Jinan Peoples R China Tencent AI Lab Bellevue WA 98004 USA Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot movements. Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction. In this paper, we propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We reduce the problem to bipartite graph matching, which establishes the node correspondences between two graphs, denoting robots and frontiers. We introduce a multiplex graph neural network (mGNN) that learns the neural distance to fill the affinity matrix for more effective graph matching. We optimize the mGNN with a differentiable linear assignment layer by maximizing the long-term values that favor time efficiency and map completeness via reinforcement learning. We compare our algorithm with several state-of-the-art multi-robot active mapping approaches and adapted reinforcement-learning baselines. Experimental results demonstrate the superior performance and exceptional generalization ability of our algorithm on various indoor scenes and unseen number of robots, when only trained with 9 indoor scenes.

关键词： Multiplexing Three-dimensional displays Machine vision Robot vision systems Estimation Reinforcement learning Sensor systems and applications

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Improving GAN Equilibrium by Raising Spatial Awareness

Improving GAN Equilibrium by Raising Spatial Awareness

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jianyuan Yang, Ceyuan Xu, Yinghao Shen, Yujun Li, Hongdong Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China Australian Natl Univ Canberra ACT Australia Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781665469463

The success of Generative Adversarial Networks (GANs) is largely built upon the adversarial training between a generator (G) and a discriminator (D). They are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, such an equilibrium is rarely achieved in practical GAN training, instead, D almost always surpasses G. We attribute one of its sources to the information asymmetry between D and G. We observe that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on for a particular synthesis. To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. Thus G can purposefully improve the synthesis of certain image regions. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the introduced spatial awareness facilitates interactive editing over the output synthesis. Denio video and code are available at https://***/eqgan-sa/.

关键词： Training Heating systems Visualization computer vision Image synthesis Computational modeling Games

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TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

TransMoMo: Invariance-Driven Unsupervised Video Motion Retar...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zhuoqian Zhu, Wentao Wu, Wayne Qian, Chen Zhou, Qiang Zhou, Bolei Loy, Chen Change Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Peking Univ Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China SenseTime Res Hong Kong Peoples R China CUHK Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728171685

We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person (Fig. I). Without using any paired data for supervision, the proposed method can be trained in an unsupervised manner by exploiting invariance properties of three orthogonal factors of variation including motion, structure, and view-angle. Specifically, with loss functions carefully derived based on invariance, we train an autoencoder to disentangle the latent representations of such factors given the source and target video clips. This allows us to selectively transfer motion extracted from the source video seamlessly to the target video in spite of structural and view-angle disparities between the source and the target. The relaxed assumption of paired data allows our method to be trained on a vast amount of videos needless of manual annotation of source-target pairing, leading to improved robustness against large structural variations and extreme motion in videos. We demonstrate the effectiveness of our method over the state-of-the-art methods such as NKN [39], EDN [7] and LCM [3]. Code, model and data are publicly available on our project page.(1)

关键词： computer vision

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Embedded vision System for Atmospheric Turbulence Mitigation 29

Embedded Vision System for Atmospheric Turbulence Mitigation

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29th ieee conference on computer vision and pattern recognition (CVPR)

作者： Deshmukh, Ajinkya Bhosale, Gaurav Medasani, Swarup Reddy, Karthik Hemanthakumar, P. Chandrasekhar, A. Kirankumar, P. Vijayasagar, K. Uurmi Syst Pvt Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509014378

Outdoor surveillance systems that involve farfield operations often encounter atmospheric turbulence perturbations due to a series of randomized reflections and refraction effecting incoming light rays. The resulting distortions make it hard to discriminate between true moving objects and turbulence induced motion. Current algorithms are not effective in detecting true moving objects in the scene and also rely on computationally complex warping methods. In this paper, we describe a real time embedded solution connected with traditional cameras to both rectify turbulence distortions and reliably detect and track true moving targets. Our comparisons with other methods shows better turbulence rectification with less false and miss detections. FPGA-DSP based embedded realization of our algorithm achieves nearly 15x speed-up along with lesser memory requirement over a quad core PC implementation. The proposed system is suitable for persistence surveillance systems and optical sight devices.

关键词： Atmospheric turbulence

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Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation

Industrial Style Transfer with Large-scale Geometric Warping...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Jinchao Guo, Fei Chen, Shuo Li, Jun Yang, Jian Nanjing Univ Sci & Technol PCA Lab Nanjing Jiangsu Peoples R China RIKEN Wako Saitama Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel style transfer method to quickly create a new visual product with a nice appearance for industrial designers reference. Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product. Our model, Industrial Style Transfer (InST), consists of large-scale geometric warping (LGW) and interest-consistency texture transfer (ICTT). LGW aims to explore an unsupervised transformation between the shape masks of the source and target products for fitting large-scale shape warping. Furthermore, we introduce a mask smoothness regularization term to prevent the abrupt changes of the details of the source product. ICTT introduces an interest regularization term to maintain important contents of the warped product when it is stylized by using the art style image. Extensive experimental results demonstrate that InST achieves state-of-the-art performance on multiple visual product design tasks, e.g., companies' snail logos and classical bottles (please see Fig. 1). To the best of our knowledge, we are the first to extend the neural style transfer method to create industrial product appearances. Code is available at https://***/InST/***

关键词： Visualization computer vision Art Porcelain Shape Pipelines Neural networks

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MDAN: Multi-level Dependent Attention Network for Visual Emotion Analysis

MDAN: Multi-level Dependent Attention Network for Visual Emo...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Liwen Wang, Zhengtao Wu, Bin Lui, Simon Tencent Mus Entertainment Innovat Technol Ctr Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual Emotion Analysis (VEA) is attracting increasing attention. One of the biggest challenges of VEA is to bridge the affective gap between visual clues in a picture and the emotion expressed by the picture. As the granularity of emotions increases, the affective gap increases as well. Existing deep approaches try to bridge the gap by directly learning discrimination among emotions globally in one shot. They ignore the hierarchical relationship among emotions at different affective levels, and the variation in the affective level of emotions to be classified. In this paper, we present the multi-level dependent attention network (MDAN) with two branches to leverage the emotion hierarchy and the correlation between different affective levels and semantic levels. The bottom-up branch directly learns emotions at the highest affective level and largely prevents hierarchy violation by explicitly following the emotion hierarchy while predicting emotions at lower affective levels. In contrast, the top-down branch aims to disentangle the affective gap by one-to-one mapping between semantic levels and affective levels, namely, Affective Semantic Mapping. A local classifier is appended at each semantic level to learn discrimination among emotions at the corresponding affective level. Then, we integrate global learning and local learning into a unified deep framework and optimize it simultaneously. Moreover, to properly model channel dependencies and spatial attention while disentangling the affective gap, we carefully designed two attention modules: the Multi-head Cross Channel Attention module and the Level-dependent Class Activation Map module. Finally, the proposed deep frame-work obtains new state-of-the-art performance on six VEA benchmarks, where it outperforms existing state-of-the-art methods by a large margin, e.g., +3.85% on the WEBEmo dataset at 25 classes classification accuracy.

关键词： Bridges Visualization computer vision Correlation Computational modeling Semantics Benchmark testing

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End-to-end weakly-supervised semantic alignment 31

End-to-end weakly-supervised semantic alignment

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Rocco, Ignacio Arandjelovic, Relja Sivic, Josef DI ENS Paris France INRIA Villers Les Nancy France DeepMind London England CTU CIIRC Prague Czech Republic PSL Res Univ CNRS Ecole Normale Super Dept Informat ENS F-75005 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic

ISBN: (纸本)9781538664209

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challenging task due to large intra-class variation, changes in viewpoint and background clutter We present the following three principal contributions. First, we develop a convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs. The outcome is that parameters are learnt from rich appearance variation present in different but semantically related images without the need for tedious manual annotation of correspondences at training time. Second, the main component of this architecture is a differentiable soft inlier scoring module, inspired by the RANSAC inlier scoring procedure, that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter. Third, we demonstrate that the proposed approach achieves state-of-the-art performance on multiple standard benchmarks for semantic alignment.

关键词： Semantics Feature extraction Training Task analysis computer architecture Clutter

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