Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision最新文献_第5页

Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction 三维边界盒预测的自回归不确定性建模

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-13 DOI: 10.48550/arXiv.2210.07424

Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, P. Abbeel, Xi Chen

引用次数: 3

SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds SWFormer:用于点云中3D物体检测的稀疏窗口转换器

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-13 DOI: 10.48550/arXiv.2210.07372

Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Drago Anguelov

{"title":"SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds","authors":"Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Drago Anguelov","doi":"10.48550/arXiv.2210.07372","DOIUrl":"https://doi.org/10.48550/arXiv.2210.07372","url":null,"abstract":"3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse nature of point occupancy within the 3D scene. In this paper, we propose Sparse Window Transformer (SWFormer ), a scalable and accurate model for 3D object detection, which can take full advantage of the sparsity of point clouds. Built upon the idea of window-based Transformers, SWFormer converts 3D points into sparse voxels and windows, and then processes these variable-length sparse windows efficiently using a bucketing scheme. In addition to self-attention within each spatial window, our SWFormer also captures cross-window correlation with multi-scale feature fusion and window shifting operations. To further address the unique challenge of detecting 3D objects accurately from sparse features, we propose a new voxel diffusion technique. Experimental results on the Waymo Open Dataset show our SWFormer achieves state-of-the-art 73.36 L2 mAPH on vehicle and pedestrian for 3D object detection on the official test set, outperforming all previous single-stage and two-stage models, while being much more efficient.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"119 17","pages":"426-442"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91408690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 39

DeepMend: Learning Occupancy Functions to Represent Shape for Repair DeepMend:学习占用函数来表示修复的形状

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05728

Nikolas Lamb, Sean Banerjee, N. Banerjee

引用次数: 3

Global Spectral Filter Memory Network for Video Object Segmentation 用于视频目标分割的全局频谱滤波记忆网络

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05567

Yong Liu, R. Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, Yujiu Yang

{"title":"Global Spectral Filter Memory Network for Video Object Segmentation","authors":"Yong Liu, R. Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, Yujiu Yang","doi":"10.48550/arXiv.2210.05567","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05567","url":null,"abstract":"This paper studies semi-supervised video object segmentation through boosting intra-frame interaction. Recent memory network-based methods focus on exploiting inter-frame temporal reference while paying little attention to intra-frame spatial dependency. Specifically, these segmentation model tends to be susceptible to interference from unrelated nontarget objects in a certain frame. To this end, we propose Global Spectral Filter Memory network (GSFM), which improves intra-frame interaction through learning long-term spatial dependencies in the spectral domain. The key components of GSFM is 2D (inverse) discrete Fourier transform for spatial information mixing. Besides, we empirically find low frequency feature should be enhanced in encoder (backbone) while high frequency for decoder (segmentation head). We attribute this to semantic information extracting role for encoder and fine-grained details highlighting role for decoder. Thus, Low (High) Frequency Module is proposed to fit this circumstance. Extensive experiments on the popular DAVIS and YouTube-VOS benchmarks demonstrate that GSFM noticeably outperforms the baseline method and achieves state-of-the-art performance. Besides, extensive analysis shows that the proposed modules are reasonable and of great generalization ability. Our source code is available at https://github.com/workforai/GSFM.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"103 1","pages":"648-665"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77418941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation DCL-Net: 6D姿态估计的深度对应学习网络

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05232

Hongyang Li, Jiehong Lin, K. Jia

{"title":"DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation","authors":"Hongyang Li, Jiehong Lin, K. Jia","doi":"10.48550/arXiv.2210.05232","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05232","url":null,"abstract":". Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step away from the true ones of object pose estimation, making the learning suboptimal for the end task. In this paper, we address this short-coming by introducing a new method of Deep Correspondence Learning Network for direct 6D object pose estimation, shortened as DCL-Net . Specifically, DCL-Net employs dual newly proposed Feature Disengagement and Alignment (FDA) modules to establish, in the feature space, partial-to-partial correspondence and complete-to-complete one for partial object observation and its complete CAD model, respectively, which result in aggregated pose and match feature pairs from two coordinate systems; these two FDA modules thus bring complementary advantages. The match feature pairs are used to learn confidence scores for measuring the qualities of deep correspondence, while the pose feature pairs are weighted by confidence scores for direct object pose regression. A confidence-based pose refinement network is also proposed to further improve pose precision in an iterative manner. Extensive experiments show that DCL-Net outperforms existing methods on three benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD; ablation studies also confirm the efficacy of our novel designs. Our code is released publicly at https://github.com/Gorilla-Lab-SCUT/DCL-Net .","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"38 1","pages":"369-385"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78053634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Map-free Visual Relocalization: Metric Pose Relative to a Single Image 无地图视觉重定位:相对于单个图像的度量姿态

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05494

Eduardo Arnold, Jamie Wynn, S. Vicente, Guillermo Garcia-Hernando, 'Aron Monszpart, V. Prisacariu, Daniyar Turmukhambetov, Eric Brachmann

{"title":"Map-free Visual Relocalization: Metric Pose Relative to a Single Image","authors":"Eduardo Arnold, Jamie Wynn, S. Vicente, Guillermo Garcia-Hernando, 'Aron Monszpart, V. Prisacariu, Daniyar Turmukhambetov, Eric Brachmann","doi":"10.48550/arXiv.2210.05494","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05494","url":null,"abstract":". Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D map. In contrast, we propose Map-free Relocalization , i.e. , using only one photo of a scene to enable instant, metric scaled relocalization. Existing datasets are not suitable to benchmark map-free relocalization, due to their focus on large scenes or their limited variability. Thus, we have constructed a new dataset of 655 small places of interest, such as sculptures, murals and fountains, collected worldwide. Each place comes with a reference image to serve as a relocalization anchor, and dozens of query images with known, metric camera poses. The dataset features changing conditions, stark viewpoint changes, high variability across places, and queries with low to no visual overlap with the reference image. We identify two viable families of existing methods to provide baseline results: relative pose regression, and feature matching combined with single-image depth prediction. While these methods show reasonable performance on some favorable scenes in our dataset, map-free relocalization proves to be a challenge that requires new, innovative solutions.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"11 1","pages":"690-708"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73238683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds LidarNAS:统一和搜索三维点云的神经架构

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-10 DOI: 10.48550/arXiv.2210.05018

Chenxi Liu, Zhaoqi Leng, Peigen Sun, Shuyang Cheng, C. Qi, Yin Zhou, Mingxing Tan, Drago Anguelov

{"title":"LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds","authors":"Chenxi Liu, Zhaoqi Leng, Peigen Sun, Shuyang Cheng, C. Qi, Yin Zhou, Mingxing Tan, Drago Anguelov","doi":"10.48550/arXiv.2210.05018","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05018","url":null,"abstract":"Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, the format of the neural features, and the neural operations used. Lack of a unified framework and interpretation makes it hard to put these designs in perspective, as well as systematically explore new ones. In this paper, we begin by proposing a unified framework of such, with the key idea being factorizing the neural networks into a series of view transforms and neural layers. We demonstrate that this modular framework can reproduce a variety of existing works while allowing a fair comparison of backbone designs. Then, we show how this framework can easily materialize into a concrete neural architecture search (NAS) space, allowing a principled NAS-for-3D exploration. In performing evolutionary NAS on the 3D object detection task on the Waymo Open Dataset, not only do we outperform the state-of-the-art models, but also report the interesting finding that NAS tends to discover the same macro-level architecture concept for both the vehicle and pedestrian classes.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"9 1","pages":"158-175"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82512092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

SCAM! Transferring humans between images with Semantic Cross Attention Modulation 骗局!语义交叉注意调制在图像间转移人

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-10 DOI: 10.48550/arXiv.2210.04883

Nicolas Dufour, David Picard, Vicky S. Kalogeiton

{"title":"SCAM! Transferring humans between images with Semantic Cross Attention Modulation","authors":"Nicolas Dufour, David Picard, Vicky S. Kalogeiton","doi":"10.48550/arXiv.2210.04883","DOIUrl":"https://doi.org/10.48550/arXiv.2210.04883","url":null,"abstract":"A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details. This is enabled by the Semantic Attention Transformer Encoder that extracts multiple latent vectors for each semantic region, and the corresponding generator that exploits these multiple latents by using semantic cross attention modulation. It is trained only using a reconstruction setup, while subject transfer is performed at test time. Our analysis shows that our proposed architecture is successful at encoding the diversity of appearance in each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new state of the art on subject transfer.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"1 1","pages":"713-729"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85072113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images 预测偏移量的超分辨率:用于栅格化图像的超高效超分辨率网络

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-09 DOI: 10.48550/arXiv.2210.04198

Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang, Yulun Zhang, Wenming Yang, Chun Yuan

{"title":"Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images","authors":"Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang, Yulun Zhang, Wenming Yang, Chun Yuan","doi":"10.48550/arXiv.2210.04198","DOIUrl":"https://doi.org/10.48550/arXiv.2210.04198","url":null,"abstract":"Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"143 1","pages":"583-598"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82897307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Attention Diversification for Domain Generalization 面向领域泛化的注意力分散

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-10-09 DOI: 10.48550/arXiv.2210.04206

Rang Meng, Xianfeng Li, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Mingli Song, Di Xie, Shiliang Pu

{"title":"Attention Diversification for Domain Generalization","authors":"Rang Meng, Xianfeng Li, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Mingli Song, Di Xie, Shiliang Pu","doi":"10.48550/arXiv.2210.04206","DOIUrl":"https://doi.org/10.48550/arXiv.2210.04206","url":null,"abstract":"Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of\"simulate, divide and assemble\": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"6 1","pages":"322-340"},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88474156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23