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Sparse Multi-Path Corrections in Fringe Projection Profilometry

Sparse Multi-Path Corrections in Fringe Projection Profilome...

引用

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

作者： Zhang, Yu Lau, Daniel Wipf, David Nanjing Univ Nanjing Peoples R China Univ Kentucky Lexington KY 40506 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper examines the phenomenon of sparse or N-modal multi-path as a more general case, where the camera pixel sees no fewer than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution by taking an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, demonstrating that it outperforms state-of-the-art techniques.

关键词： Linear systems Surface reconstruction computer vision Lighting Cameras Mathematical models pattern recognition

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Learning To Count Everything

Learning To Count Everything

引用

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

作者： Ranjan, Viresh Sharma, Udbhav Thu Nguyen Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781665445092

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://***/cvlab-stonybrook/LearningToCountEverything.

关键词： Visualization computer vision Codes Annotations Animals Detectors pattern recognition

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Learning Progressive Point Embeddings for 3D Point Cloud Generation

Learning Progressive Point Embeddings for 3D Point Cloud Gen...

引用

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

作者： Wen, Cheng Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci 6 Cleveland St Darlington NSW 2008 Australia

ISBN: (纸本)9781665445092

Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clouds, because of the difficulties in 1) learning effective pointwise representations;and 2) generating realistic point clouds from complex distributions. In this paper, we devise a dual-generators framework for point cloud generation, which generalizes vanilla generative adversarial learning framework in a progressive manner. Specifically, the first generator aims to learn effective point embeddings in a breadth-first manner, while the second generator is used to refine the generated point cloud based on a depth-first point embedding to generate a robust and uniform point cloud. The proposed dual-generators framework thus is able to progressively learn effective point embeddings for accurate point cloud generation. Experimental results on a variety of object categories from the most popular point cloud generation dataset, ShapeNet, demonstrate the state-of-the-art performance of the proposed method for accurate point cloud generation.

关键词： Deep learning Solid modeling computer vision Three-dimensional displays Generators Adversarial machine learning pattern recognition

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Stochastic Whitening Batch Normalization

Stochastic Whitening Batch Normalization

引用

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

作者： Zhang, Shengdong Nezhadarya, Ehsan Fashandi, Homa Liu, Jiayi Graham, Darin Shah, Mohak LG Elect Canada Toronto AI Lab Toronto ON Canada LG Elect USA Amer R&D Lab Santa Clara CA USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton's method. However, since Newton's method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks.

关键词： Training Deep learning computer vision Stochastic processes pattern recognition Iterative methods Convolutional neural networks

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ArtCoder: An End-to-end Method for Generating Scanning-robust Stylized QR Codes

ArtCoder: An End-to-end Method for Generating Scanning-robus...

引用

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

作者： Su, Hao Niu, Jianwei Liu, Xuefeng Li, Qingfeng Wan, Ji Xu, Mingliang Ren, Tao Beihang Univ Sch Comp Sci & Engn State Key Lab VR Technol & Syst Beijing Peoples R China Zhengzhou Univ Sch Informat Engn Ind Technol Res Inst Zhengzhou Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works adopt fixed generation algorithms and therefore can only generate QR codes with a pre-defined style. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end method, named ArtCoder, to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. To guarantee that the generated stylized QR codes are still scanning-robust, we propose a Sampling-Simulation layer, a module-based code loss, and a competition mechanism. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.

关键词： computer vision Codes Semantics Diversity reception Visual effects pattern recognition

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StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Imag...

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

作者： Wu, Zongze Lischinski, Dani Shechtman, Eli Hebrew Univ Jerusalem Jerusalem Israel Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.

关键词： Measurement Visualization computer vision Analytical models Image synthesis computer architecture Aerospace electronics

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Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods

Enhance Curvature Information by Structured Stochastic Quasi...

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

作者： Yang, Minghan Xu, Dong Chen, Hongyu Wen, Zaiwen Chen, Mengyun Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods.

关键词： Training computer vision pattern recognition Convolutional neural networks Artificial intelligence Portfolios Optimization

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TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

TrafficSim: Learning to Simulate Realistic Multi-Agent Behav...

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

作者： Suo, Simon Regalado, Sebastian Casas, Sergio Urtasun, Raquel Uber ATG Pittsburgh PA 15201 USA Univ Toronto Toronto ON Canada Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

Simulation has the potential to massively scale evaluation of self-driving systems, enabling rapid development as well as safe deployment. Bridging the gap between simulation and the real world requires realistic multi-agent behaviors. Existing simulation environments rely on heuristic-based models that directly encode traffic rules, which cannot capture irregular maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding, merging). In contrast, we leverage real-world data to learn directly from human demonstration, and thus capture more naturalistic driving behaviors. To this end, we propose TRAFFICSIM, a multi-agent behavior model for realistic traffic simulation. In particular, we parameterize the policy with an implicit latent variable model that generates socially-consistent plans for all actors in the scene jointly. To learn a robust policy amenable for long horizon simulation, we unroll the policy in training and optimize through the fully differentiable simulation across time. Our learning objective incorporates both human demonstrations as well as common sense. We show TRAFFICSIM generates significantly more realistic traffic scenarios as compared to a diverse set of baselines. Notably, we can exploit trajectories generated by TRAFFICSIM as effective data augmentation for training better motion planner.

关键词： Training computer vision Merging Traffic control Trajectory Safety pattern recognition

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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

PAConv: Position Adaptive Convolution with Dynamic Kernel As...

引用

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

作者： Xu, Mutian Ding, Runyu Zhao, Hengshuang Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing. The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet. In this way, the kernel is built in a data-driven manner, endowing PAConv with more flexibility than 2D convolutions to better handle the irregular and unordered point cloud data. Besides, the complexity of the learning process is reduced by combining weight matrices instead of brutally predicting kernels from point positions. Furthermore, different from the existing point convolution operators whose network architectures are often heavily engineered, we integrate our PAConv into classical MLP-based point cloud pipelines without changing network configurations. Even built on simple networks, our method still approaches or even surpasses the state-of-the-art models, and significantly improves baseline performance on both classification and segmentation tasks, yet with decent efficiency. Thorough ablation studies and visualizations are provided to understand PAConv. Code is released on hups://***/CVMI-Lab/PAConv.

关键词： computer vision Three-dimensional displays Convolution Computational modeling Pipelines Network architecture pattern recognition

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Intrinsic Image Harmonization

Intrinsic Image Harmonization

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

作者： Guo, Zonghui Zheng, Haiyong Jiang, Yufeng Gu, Zhaorui Zheng, Bing Ocean Univ China Underwater Vis Lab Qingdao Shandong Peoples R China Ocean Univ China Sanya Oceanog Inst Qingdao Shandong Peoples R China

ISBN: (纸本)9781665445092

Compositing an image usually inevitably suffers from inharmony problem that is mainly caused by incompatibility of foreground and background from two different images with distinct surfaces and lights, corresponding to material-dependent and light-dependent characteristics, namely, reflectance and illumination intrinsic images, respectively. Therefore, we seek to solve image harmonization via separable harmonization of reflectance and illumination, i.e., intrinsic image harmonization. Our method is based on an autoencoder that disentangles composite image into reflectance and illumination for further separate harmonization. Specifically, we harmonize reflectance through material-consistency penalty, while harmonize illumination by learning and transferring light from background to foreground, moreover, we model patch relations between foreground and background of composite images in an inharmony-free learning way, to adaptively guide our intrinsic image harmonization. Both extensive experiments and ablation studies demonstrate the power of our method as well as the efficacy of each component. We also contribute a new challenging dataset for benchmarking illumination harmonization. Code and dataset are at https://***/zhenglab/IntrinsicHarmony.

关键词： Reflectivity computer vision Adaptation models Codes Lighting Benchmark testing pattern recognition

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