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Indoor Visual Localization using Point and Line Correspondences in dense colored point cloud

Indoor Visual Localization using Point and Line Corresponden...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Matsumoto, Yuya Nakano, Gaku Ogura, Kazumine NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

We propose a novel pipeline called Loc-PL that uses both points and lines for indoor visual localization in dense colored point cloud. Loc-PL utilizes the spatially complementary relationship between points and lines to address challenging indoor issues. There are two successive camera pose estimation modules. The first improves robustness against repetitive patterns by considering the geometric consistency of points and lines. The second utilizes points and lines to refine poses by Perspective-m-Point-n-Line (PmPnL) and circumvents unstable localization due to locally concentrated matches caused by less-textured environments. The modules use different schemes to obtain line correspondences;the first finds line matches using RANSAC, which is effective for image pairs with large viewpoint gaps, and the second utilizes rendered images from dense point cloud to get them by feature line matching. In addition, we develop a simple but effective module for evaluating the correctness of camera poses using matched point distances across two images. The experimental results on a large dataset, InLoc, show that Loc-PL achieves the state-of-the-art in four out of six scores.

关键词： 3D computer vision Algorithms applications Robotics

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BSRAW: Improving Blind RAW Image Super-Resolution

BSRAW: Improving Blind RAW Image Super-Resolution

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Conde, Marcos, V Vasluianu, Florin Timofte, Radu Univ Wurzburg Comp Vis Lab CAIDAS Wurzburg Germany Univ Wurzburg IFI Wurzburg Germany

ISBN: (纸本)9798350318920;9798350318937

In smartphones and compact cameras, the Image Signal Processor (ISP) transforms the RAW sensor image into a human-readable sRGB image. Most popular super-resolution methods depart from a sRGB image and upscale it further, improving its quality. However, modeling the degradations in the sRGB domain is complicated because of the non-linear ISP transformations. Despite this known issue, only a few methods work directly with RAW images and tackle real-world sensor degradations. We tackle blind image super-resolution in the RAW domain. We design a realistic degradation pipeline tailored specifically for training models with raw sensor data. Our approach considers sensor noise, defocus, exposure, and other common issues. Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality. As part of this effort, we also present a new DSLM dataset and benchmark for this task.

关键词： applications applications Embedded sensing / real-time techniques Smartphones / end user devices

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Uncertainty Estimation in Instance Segmentation with Star-convex Shapes

Uncertainty Estimation in Instance Segmentation with Star-co...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Siddiqui, Qasim M. K. Starke, Sebastian Steinbach, Peter Helmholtz Zentrum Dresden Rossendorf Dresden Germany

ISBN: (纸本)9798350318920;9798350318937

Instance segmentation has witnessed promising advancements through deep neural network-based algorithms. However, these models often exhibit incorrect predictions with unwarranted confidence levels. Consequently, evaluating prediction uncertainty becomes critical for informed decision-making. Existing methods primarily focus on quantifying uncertainty in classification or regression tasks, lacking emphasis on instance segmentation. Our research addresses the challenge of estimating spatial certainty associated with the location of instances with star-convex shapes. Two distinct clustering approaches are evaluated which compute spatial and fractional certainty per instance employing samples by the Monte-Carlo Dropout or Deep Ensemble technique. Our study demonstrates that combining spatial and fractional certainty scores yields improved calibrated estimation over individual certainty scores. Notably, our experimental results show that the Deep Ensemble technique alongside our novel radial clustering approach proves to be an effective strategy. Our findings emphasize the significance of evaluating the calibration of estimated certainties for model reliability and decision-making.

关键词： Algorithms applications applications Biomedical / healthcare / medicine Image recognition and understanding Visualization

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Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement

Leveraging Bitstream Metadata for Fast, Accurate, Generalize...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Ehrlich, Max Barker, Jon Padmanabhan, Namitha Davis, Larry Tao, Andrew Catanzarole, Bryan Shrivastava, Abhinav NVIDA Santa Clara CA 95051 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350318920;9798350318937

Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many compression settings, quality loss is still noticeable. These settings nevertheless have important applications to the efficient transmission of videos over bandwidth constrained or otherwise unstable connections. In this work, we develop a deep learning architecture capable of restoring detail to compressed videos which leverages the underlying structure and motion information embedded in the video bitstream. We show that this improves restoration accuracy compared to prior compression correction methods and is competitive when compared with recent deep-learning-based video compression methods on rate-distortion while achieving higher throughput. Furthermore, we condition our model on quantization data which is readily available in the bitstream. This allows our single model to handle a variety of different compression quality settings which required an ensemble of models in prior work.

关键词： Algorithms Algorithms applications Computational photography image and video synthesis Low-level and physics-based vision Smartphones / end user devices

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Simple Token-Level Confidence Improves Caption Correctness

Simple Token-Level Confidence Improves Caption Correctness

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Petryk, Suzanne Whitehead, Spencer Gonzalez, Joseph E. Darrell, Trevor Rohrbach, Anna Rohrbach, Marcus Univ Calif Berkeley Berkeley CA 94720 USA Meta Menlo Pk CA 94025 USA

ISBN: (纸本)9798350318920;9798350318937

The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the correctness of fine-grained details, leading to errors in outputs such as hallucinating objects in generated captions or poor compositional reasoning. In this work, we explore Token-Level Confidence, or TLC, as a simple yet surprisingly effective method to assess caption correctness. Specifically, we fine-tune a vision-language model on image captioning, input an image and proposed caption to the model, and aggregate either algebraic or learned token confidences over words or sequences to estimate image-caption consistency. Compared to sequence-level scores from pretrained models, TLC with algebraic confidence measures achieves a relative improvement in accuracy by 10% on verb understanding in SVO-Probes and more than doubles image and group scores for compositional reasoning in Winoground. When training data are available, a learned confidence estimator provides further improved performance, reducing object hallucination rates in MS COCO Captions by a relative 30% over the original model and setting a new state-of-the-art.

关键词： Algorithms vision + language and/or other modalities

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Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings

Real Time GAZED: Online Shot Selection and Editing of Virtua...

引用

ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Achary, Sudheer Girmaji, Rohit Deshmukh, Adhiraj Anil Gandhi, Vineet IIIT Hyderabad CVIT KCIS Hyderabad Telangana India

ISBN: (纸本)9798350318920;9798350318937

Eliminating time-consuming post-production processes and delivering high-quality videos in today's fast-paced digital landscape are the key advantages of real-time approaches. To address these needs, we present Real Time GAZED: a real-time adaptation of the GAZED framework integrated with CineFilter, a novel real-time camera trajectory stabilization approach. It enables users to create professionally edited videos in real-time. Comparative evaluations against baseline methods, including the non-real-time GAZED, demonstrate that Real Time GAZED achieves similar editing results, ensuring high-quality video output. Furthermore, a user study confirms the aesthetic quality of the video edits produced by the Real Time GAZED approach. With these advancements in real-time camera trajectory optimization and video editing presented, the demand for immediate and dynamic content creation in industries such as live broadcasting, sports coverage, news reporting, and social media content creation can be met more efficiently.

关键词： Algorithms applications applications Computational photography Embedded sensing / real-time techniques image and video synthesis Virtual / augmented reality

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WaveMixSR: Resource-efficient Neural Network for Image Super-resolution

WaveMixSR: Resource-efficient Neural Network for Image Super...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Jeevan, Pranav Srinidhi, Akella Prathiba, Pasunuri Sethi, Amit Indian Inst Technol Mumbai India

ISBN: (纸本)9798350318920;9798350318937

Image super-resolution research recently has been dominated by transformer models which need higher computational resources than CNNs due to the quadratic complexity of self-attention. We propose a new neural network - WaveMixSR - for image super-resolution based on the WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing. Unlike transformer-based models, WaveMixSR does not unroll the image as a sequence of pixels/patches. It uses the inductive bias of convolutions along with the lossless token-mixing property of wavelet transform to achieve higher performance while requiring fewer resources and training data. We compare the performance of our network with other state-of-the-art methods for image super-resolution. Our experiments show that WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks. Our model is able to achieve this performance using less training data and computational resources while maintaining high parameter efficiency compared to current state-of-the-art models.

关键词： applications applications Social good Visualization

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Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

Synthesizing Coherent Story with Auto-Regressive Latent Diff...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Pan, Xichen Qin, Pengda Lie, Yuhong Xue, Hui Chen, Wenhu NYU New York NY 10012 USA Alibaba Grp Hangzhou Peoples R China Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350318920;9798350318937

Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images;While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks and propose AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images. Moreover, AR-LDM can generalize to new characters through adaptation. To our best knowledge, this is the first work successfully leveraging diffusion models for coherent visual story synthesizing. It also extends the text-conditioned method to multimodal conditioning. Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the adopted challenging dataset VIST containing natural images. Large-scale human evaluations show that AR-LDM has superior performance in terms of quality, relevance, and consistency. Code available at this https URL

关键词： 3D Algorithms Algorithms applications etc. Generative models for image video vision + language and/or other modalities Visualization

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Detecting Content Segments from Online Sports Streaming Events: Challenges and Solutions

Detecting Content Segments from Online Sports Streaming Even...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Liu, Zongyi Feng, Yarong Luo, Shunyan Ling, Yuan Dong, Shujing Wang, Shuyi Amazon Com Customer Experience & Business Trends 2121 7th Ave Seattle WA 98121 USA

ISBN: (纸本)9798350318920;9798350318937

Developing a client-side segmentation algorithm for online sports streaming holds significant importance. For instance, in order to assess the video quality from an end-user perspective such as artifact detection, it is important to initially segment the content within the streaming playback. The challenge lies in localizing the content due to the intricate scene changes between content and non-content sections in popular sports like football, tennis, baseball, and more. Client-side content detection can be implemented in two ways: intrusively, involving the interception of network traffic and parsing service provider data and logs, or non-intrusively, which entails capturing streamed videos from content providers and subjecting them to analysis using computer vision technologies. In this paper, we introduce a non-intrusive framework that leverages a combination of traditional machine learning algorithms and deep neural networks (DNN) to distinguish content sections from non-content sections across various online sports streaming services. Our algorithm has demonstrated a remarkable level of accuracy and effectiveness in sports broadcasting events, effectively overcoming the complexities introduced by intricate non-content insertion methods during the games.

关键词： Algorithms Algorithms applications Smartphones / end user devices Video recognition and understanding vision + language and/or other modalities

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Self-Annotated 3D Geometric Learning for Smeared Points Removal

Self-Annotated 3D Geometric Learning for Smeared Points Remo...

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ieee/CVF Winter conference on applications of computer vision (WACV)

作者： Wang, Miaowei Morris, Daniel Univ Edinburgh Edinburgh Midlothian Scotland Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350318920;9798350318937

There has been significant progress in improving the accuracy and quality of consumer-level dense depth sensors. Nevertheless, there remains a common depth pixel artifact which we call smeared points. These are points not on any 3D surface and typically occur as interpolations between foreground and background objects. As they cause fictitious surfaces, these points have the potential to harm applications dependent on the depth maps. Statistical outlier removal methods fare poorly in removing these points as they tend also to remove actual surface points. Trained network-based point removal faces difficulty in obtaining sufficient annotated data. To address this, we propose a fully self-annotated method to train a smeared point removal classifier. Our approach relies on gathering 3D geometric evidence from multiple perspectives to automatically detect and annotate smeared points and valid points. To validate the effectiveness of our method, we present a new benchmark dataset: the Real Azure-Kinect dataset. Experimental results and ablation studies show that our method outperforms traditional filters and other self-annotated methods. Our work is publicly available at https://***/wangmiaowei/wacv2024_***.

关键词： 3D computer vision Algorithms Algorithms applications Image recognition and understanding Remote Sensing

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