2020 International Conference on 3D Vision (3DV)最新文献

Differential Photometric Consistency 差光度一致性

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00023

Hongyi Fan, B. Kunsberg, B. Kimia

{"title":"Differential Photometric Consistency","authors":"Hongyi Fan, B. Kunsberg, B. Kimia","doi":"10.1109/3DV50981.2020.00023","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00023","url":null,"abstract":"A key bottleneck in the use of Multiview Stereo (MVS) to produce high quality reconstructions is the gaps arising from textureless, shaded areas and lack of fine-scale detail. Shape-from-Shading (SfS) has been used in conjunction with MVS to obtain fine-scale detail and veridical reconstruction in the gap areas. The similarity metric that gauges candidate correspondences is critical to this process, typically a combination of photometric consistency and brightness gradient constancy. Two observations motivate this paper. First, brightness gradient constancy can be erroneous due to foreshortening. Second, the standard ZSSD/NCC patchwise photometric consistency measures when applied to shaded areas is, to a first-order approximation, a calculation of brightness gradient differences, which can be subject to foreshortening. The paper proposes a novel trinocular differential photometric consistency that constrains the brightness gradients in three views so that the image gradient in one view is completely determined by the image gradients at corresponding points in the the other two views. The theoretical developments here advocate the integration of this new measure, whose viability in practice has been demonstrated in a set of illustrative numerical experiments.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116861183","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: 0

RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty RidgeSfM:基于深度不确定性的鲁棒成对匹配的运动结构

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00075

Benjamin Graham, David Novotný

{"title":"RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty","authors":"Benjamin Graham, David Novotný","doi":"10.1109/3DV50981.2020.00075","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00075","url":null,"abstract":"We consider the problem of simultaneously estimating a dense depth map and camera pose for a large set of images of an indoor scene. While classical SfM pipelines rely on a two-step approach where cameras are first estimated using a bundle adjustment in order to ground the ensuing multi-view stereo stage, both our poses and dense reconstructions are a direct output of an altered bundle adjuster. To this end, we parametrize each depth map with a linear combination of a limited number of basis “depth-planes” predicted in a monocular fashion by a deep net. Using a set of high-quality sparse keypoint matches, we optimize over the per-frame linear combinations of depth planes and camera poses to form a geometrically consistent cloud of keypoints. Although our bundle adjustment only considers sparse keypoints, the inferred linear coefficients of the basis planes immediately give us dense depth maps. RidgeSfM is able to collectively align hundreds of frames, which is its main advantage over recent memory-heavy deep alternatives that are typically capable of aligning no more than 10 frames. Quantitative comparisons reveal performance superior to a state-of-the-art large-scale SfM pipeline.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121033629","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

FC-vSLAM: Integrating Feature Credibility in Visual SLAM FC-vSLAM:集成视觉SLAM中的特征可信度

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00106

Shuai Xie, Wei Ma, Qiuyuan Wang, Ruchang Xu, H. Zha

引用次数: 0

Deep LiDAR localization using optical flow sensor-map correspondences 使用光流传感器-地图对应的深度激光雷达定位

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00094

Anders Sunegård, L. Svensson, Torsten Sattler

{"title":"Deep LiDAR localization using optical flow sensor-map correspondences","authors":"Anders Sunegård, L. Svensson, Torsten Sattler","doi":"10.1109/3DV50981.2020.00094","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00094","url":null,"abstract":"In this paper we propose a method for accurate localization of a multi-layer LiDAR sensor in a pre-recorded map, given a coarse initialization pose. The foundation of the algorithm is the usage of neural network optical flow predictions. We train a network to encode representations of the sensor measurement and the map, and then regress flow vectors at each spatial position in the sensor feature map. The flow regression network is straight-forward to train, and the resulting flow field can be used with standard techniques for computing sensor pose from sensor-to-map correspondences. Additionally, the network can regress flow at different spatial scales, which means that it is able to handle both position recovery and high accuracy localization. We demonstrate average localization accuracy of $lt 0.04{mathrm {m}}$ position and $lt 0.1^{circ}$ heading angle for a vehicle driving application with simulated LiDAR measurements, which is similar to point-to-point iterative closest point (ICP). The algorithm typically manages to recover position with prior error of more than 20m and is significantly more robust to scenes with non-salient or repetitive structure than the baselines used for comparison.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565908","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: 0

Learning Wasserstein Isometric Embedding for Point Clouds 点云的Wasserstein等距嵌入学习

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00057

Keisuke Kawano, Satoshi Koide, Takuro Kutsuna

引用次数: 3

Localising In Complex Scenes Using Balanced Adversarial Adaptation 在复杂场景中使用平衡对抗适应进行定位

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00116

Gil Avraham, Yan Zuo, T. Drummond

{"title":"Localising In Complex Scenes Using Balanced Adversarial Adaptation","authors":"Gil Avraham, Yan Zuo, T. Drummond","doi":"10.1109/3DV50981.2020.00116","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00116","url":null,"abstract":"Domain adaptation and generative modelling have collectively mitigated the expensive nature of data collection and labelling by leveraging the rich abundance of accurate, labelled data in simulation environments. In this work, we study the performance gap that exists between representations optimised for localisation on simulation environments and the application of such representations in a real-world setting. Our method exploits the shared geometric similarities between simulation and real-world environments whilst maintaining invariance towards visual discrepancies. This is achieved by optimising a representation extractor to project both simulated and real representations into a shared representation space. Our method uses a symmetrical adversarial approach which encourages the representation extractor to conceal the domain that features are extracted from and simultaneously preserves robust attributes between source and target domains that are beneficial for localisation. We evaluate our method by adapting representations optimised for indoor Habitat simulated environments (Matterport3D and Replica) to a real-world indoor environment (Active Vision Dataset), showing that it compares favourably against fully-supervised approaches.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243314","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: 0

Deep Learning Based Single-Photon 3D Imaging with Multiple Returns 基于深度学习的多收益单光子三维成像

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00130

Hao Tan, Jiayong Peng, Zhiwei Xiong, Dong Liu, Xin Huang, Zheng-Ping Li, Yu Hong, Feihu Xu

{"title":"Deep Learning Based Single-Photon 3D Imaging with Multiple Returns","authors":"Hao Tan, Jiayong Peng, Zhiwei Xiong, Dong Liu, Xin Huang, Zheng-Ping Li, Yu Hong, Feihu Xu","doi":"10.1109/3DV50981.2020.00130","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00130","url":null,"abstract":"photon avalanche diode (SPAD) has been widely used in active 3D imaging due to its extremely high photon sensitivity and picosecond time resolution. However, long-range active 3D imaging is still a great challenge, since only a few signal photons mixed with strong background noise can return from multiple reflectors of the scene due to the divergence of the light beam and the receiver’s field of view (FoV), which would bring considerable distortion and blur to the recovered depth map. In this paper, we propose a deep learning based depth reconstruction method for long range single-photon 3D imaging where the “multiple-returns” issue exists. Specifically, we model this problem as a deblurring task and design a multi-scale convolutional neural network combined with elaborate loss functions, which promote the reconstruction of an accurate depth map with fine details and clear boundaries of objects. The proposed method achieves superior performance over several different sizes of receiver’s FoV on a synthetic dataset compared with existing state-of-the-art methods and the trained model under a specific FoV has a strong generalization capability across different sizes of FoV, which is essential for practical applications. Moreover, we conduct outdoor experiments and demonstrate the effectiveness of our method in a real-world long range imaging system.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121771878","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

Benchmarking Image Retrieval for Visual Localization 面向视觉定位的基准图像检索

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00058

No'e Pion, M. Humenberger, G. Csurka, Yohann Cabon, Torsten Sattler

{"title":"Benchmarking Image Retrieval for Visual Localization","authors":"No'e Pion, M. Humenberger, G. Csurka, Yohann Cabon, Torsten Sattler","doi":"10.1109/3DV50981.2020.00058","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00058","url":null,"abstract":"Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two tasks: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for these tasks. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes. However, robustness to viewpoint changes is not necessarily desirable in the context of visual localization. This paper focuses on understanding the role of image retrieval for multiple visual localization tasks. We introduce a benchmark setup and compare state-of-the-art retrieval representations on multiple datasets. We show that retrieval performance on classical landmark retrieval/recognition tasks correlates only for some but not all tasks to localization performance. This indicates a need for retrieval approaches specifically designed for localization tasks. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization.","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132479216","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: 44

Fast Simultaneous Gravitational Alignment of Multiple Point Sets 多点集快速同步重力对准

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00019

Vladislav Golyanik, Soshi Shimada, C. Theobalt

{"title":"Fast Simultaneous Gravitational Alignment of Multiple Point Sets","authors":"Vladislav Golyanik, Soshi Shimada, C. Theobalt","doi":"10.1109/3DV50981.2020.00019","DOIUrl":"https://doi.org/10.1109/3DV50981.2020.00019","url":null,"abstract":"The problem of simultaneous rigid alignment of multiple unordered point sets which is unbiased towards any of the inputs has recently attracted increasing interest, and several reliable methods have been newly proposed. While being remarkably robust towards noise and clustered outliers, current approaches require sophisticated initialisation schemes and do not scale well to large point sets. This paper proposes a new resilient technique for simultaneous registration of multiple point sets by interpreting the latter as particle swarms rigidly moving in the mutually induced force fields. Thanks to the improved simulation with altered physical laws and acceleration of globally multiply-linked point interactions with a 2D-tree (D is the space dimensionality), our Multi-Body Gravitational Approach (MBGA) is robust to noise and missing data while supporting more massive point sets than previous methods (with 105 points and more). In various experimental settings, MBGA is shown to outperform several baseline point set alignment approaches in terms of accuracy and runtime. We make our source code available for the community to facilitate the reproducibility of the results1.1http://gvv.mpi-inf.mpg.de/projects/MBGA/","PeriodicalId":293399,"journal":{"name":"2020 International Conference on 3D Vision (3DV)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132904503","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: 1

A Transformer-Based Network for Dynamic Hand Gesture Recognition 基于变压器的动态手势识别网络

2020 International Conference on 3D Vision (3DV) Pub Date : 2020-11-01 DOI: 10.1109/3DV50981.2020.00072

Andrea D'Eusanio, A. Simoni, S. Pini, G. Borghi, R. Vezzani, R. Cucchiara

引用次数: 20