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Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering

Envisioning a Next Generation Extended Reality Conferencing ...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Shen, Chuanyue Zhang, Letian Yang, Zhangsihao Mortazavi, Masood Song, Xiyun Peng, Liang Yu, Heather University of Illinois at Urbana-Champaign United States University of Miami United States Arizona State University United States Futurewei Technologies United States

ISBN: (纸本)9798350302493

Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world. © 2023 ieee.

关键词： Three dimensional computer graphics

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Bush Detection for vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Bush Detection for Vision-based UGV Guidance in Blueberry Or...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Filipović, Vladan Stefanović, Dimitrije Pajević, Nina Grbović, Željana Djuric, Nemanja Panić, Marko BioSense Institute Novi Sad Serbia

ISBN: (纸本)9798350302493

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications. © 2023 ieee.

关键词： Object detection

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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Tun Chen, Rongshan Cong, Ruixuan Yang, Da Cui, Zhenglong Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset. © 2023 ieee.

关键词： Cost estimating

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Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics i...

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23rd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Melzi, Pietro Shahreza, Hatef Otroshi Rathgeb, Christian Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Marcel, Sebastien Busch, Christoph Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain Idiap Res Inst Biometr Secur & Privacy Grp Martigny Switzerland Ecole Polytech Fed Lausanne Sch Engn Lausanne Switzerland Hsch Darmstadt HDA Biometr & Secur Res Grp Da Sec Darmstadt Germany Univ Lausanne UNIL Sch Criminal Justice Lausanne Switzerland Norwegian Univ Sci & Technol NTNU Norwegian Biometr Lab Trondheim Norway

ISBN: (纸本)9798350320565

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose MultiIVE, a new method based on IVE to secure multiple softbiometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric attributes contained in face embeddings. In particular, we consider a domain transformation using Principle Component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by stateof-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultaneously secure multiple soft-biometric attributes and support the application of embedding domain transformations before addressing the enhancement of soft-biometric privacy.

关键词： Privacy Machine learning algorithms Face recognition Machine learning Feature extraction Prediction algorithms Classification algorithms

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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Ob...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Yu, Kaicheng Tao, Tang Xie, Hongwei Lin, Zhiwei Liang, Tingting Wang, Bing Chen, Peng Hao, Dayang Wang, Yongtao Liang, Xiaodan Sun Yat-sen University Shenzhen Campus China Alibaba Group Autonomous Driving Lab China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350302493

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input;ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://***/robust-benchmark-website. © 2023 ieee.

关键词： Cameras

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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

Pano3D: A Holistic Benchmark and a Solid Baseline for 360° ...

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

作者： Albanis, Georgios Zioulis, Nikolaos Drakoulis, Petros Gkitsas, Vasileios Sterzentsenko, Vladimiros Alvarez, Federico Zarpalas, Dimitrios Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781665448994

Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degrees depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

关键词： computer vision conferences Estimation Benchmark testing Solids pattern recognition

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Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding

Hierarchical Semantic Correspondence Networks for Video Para...

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

作者： Tan, Chaolei Lin, Zihang Hu, Jian-Fang Zheng, Wei-Shi Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS.

关键词： and reasoning language vision

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Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization

Learning to Exploit the Sequence-Specific Prior Knowledge fo...

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

作者： Qin, Haina Han, Longfei Xiong, Weihua Wang, Juan Ma, Wentao Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China Zeku Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks.

关键词： Low-level vision

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Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval

Exposing and Mitigating Spurious Correlations for Cross-Moda...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kim, Jae Myung Sophia Koepke, A. Schmid, Cordelia Akata, Zeynep University of Tübingen Germany PSL Research University Inria Ecole Normale Supérieure CNRS France MPI for Intelligent Systems Germany

ISBN: (纸本)9798350302493

Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly learn to memorize spurious correlations in the training data, such as frequent object co-occurrence, instead of looking at the actual underlying reasons for the prediction in the image. For image-text retrieval, this manifests in retrieved sentences that mention objects that are not present in the query image. In this work, we introduce ODmAP@k, an object decorrelation metric that measures a model's robustness to spurious correlations in the training data. We use automatic image and text manipulations to control the presence of such object correlations in designated test data. Additionally, our data synthesis technique is used to tackle model biases due to spurious correlations of semantically unrelated objects in the training data. We apply our proposed pipeline, which involves the finetuning of image-text retrieval frameworks on carefully designed synthetic data, to three state-of-the-art models for image-text retrieval. This results in significant improvements for all three models, both in terms of the standard retrieval performance and in terms of our object decorrelation metric. The code is available at https://***/ExplainableML/Spurious-CM-Retrieval. © 2023 ieee.

关键词： computer vision

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NTIRE 2022 Spectral Demosaicing Challenge and Data Set

NTIRE 2022 Spectral Demosaicing Challenge and Data Set

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Wu, Yaqi Wu, Xun Fan, Zhihao Xia, Chenjie Zhang, Feng Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Zhang, Mingwei Feng, Kai Zhang, Xun Yao, Jiaxin Zhao, Yongqiang Ma, Suina He, Fan Dong, Yangyang Yu, Shufang Qiu, Difa Liu, Jinhui Bi, Mengzhao Song, Beibei Sun, WenFang Zheng, Jiesi Zhao, Bowen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Kong, Xiangyu Yu, Jingbo Xue, Yuanyang Xie, Zheng Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Swiss Fed Inst Technol Computat Vision Lab Zurich Switzerland Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Harbin Inst Technol Harbin 150001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Xiaomi Inc Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China Changan Univ Xian Peoples R China Xidian Univ Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.

关键词： Performance evaluation Training Runtime Neural networks Cameras Real-time systems pattern recognition

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