2016 Fourth International Conference on 3D Vision (3DV)最新文献_第2页

Optical Flow for Rigid Multi-Motion Scenes 刚性多运动场景的光流

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.30

Tomas Gerlich, Jakob Eriksson

引用次数: 1

Learning a General-Purpose Confidence Measure Based on O(1) Features and a Smarter Aggregation Strategy for Semi Global Matching 基于O(1)特征的通用置信度学习和半全局匹配的智能聚合策略

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.61

Matteo Poggi, S. Mattoccia

{"title":"Learning a General-Purpose Confidence Measure Based on O(1) Features and a Smarter Aggregation Strategy for Semi Global Matching","authors":"Matteo Poggi, S. Mattoccia","doi":"10.1109/3DV.2016.61","DOIUrl":"https://doi.org/10.1109/3DV.2016.61","url":null,"abstract":"Inferring dense depth from stereo is crucial for several computer vision applications and Semi Global Matching (SGM) is often the preferred choice due to its good tradeoff between accuracy and computation requirements. Nevertheless, it suffers of two major issues: streaking artifacts caused by the Scanline Optimization (SO) approach, at the core of this algorithm, may lead to inaccurate results and the high memory footprint that may become prohibitive with high resolution images or devices with constrained resources. In this paper, we propose a smart scanline aggregation approach for SGM aimed at dealing with both issues. In particular, the contribution of this paper is threefold: i) leveraging on machine learning, proposes a novel generalpurpose confidence measure suited for any for stereo algorithm, based on O(1) features, that outperforms state of-the-art ii) taking advantage of this confidence measure proposes a smart aggregation strategy for SGM enabling significant improvements with a very small overhead iii) the overall strategy drastically reduces the memory footprint of SGM and, at the same time, improves its effectiveness and execution time. We provide extensive experimental results, including a cross-validation with multiple datasets (KITTI 2012, KITTI 2015 and Middlebury 2014).","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419881","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: 61

Structure from Category: A Generic and Prior-Less Approach 从类别结构:一个通用的和无先验的方法

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.38

Chen Kong, Rui Zhu, Hamed Kiani Galoogahi, S. Lucey

{"title":"Structure from Category: A Generic and Prior-Less Approach","authors":"Chen Kong, Rui Zhu, Hamed Kiani Galoogahi, S. Lucey","doi":"10.1109/3DV.2016.38","DOIUrl":"https://doi.org/10.1109/3DV.2016.38","url":null,"abstract":"Inferring the motion and shape of non-rigid objects from images has been widely explored by Non-Rigid Structure from Motion (NRSfM) algorithms. Despite their promising results, they often utilize additional constraints about the camera motion (e.g. temporal order) and the deformation of the object of interest, which are not always provided in real-world scenarios. This makes the application of NRSfM limited to very few deformable objects (e.g. human face and body). In this paper, we propose the concept of Structure from Category (SfC) to reconstruct 3D structure of generic objects solely from images with no shape and motion constraint (i.e. prior-less). Similar to the NRSfM approaches, SfC involves two steps: (i) correspondence, and (ii) inversion. Correspondence determines the location of key points across images of the same object category. Once established, the inverse problem of recovering the 3D structure from the 2D points is solved over an augmented sparse shape-space model. We validate our approach experimentally by reconstructing 3D structures of both synthetic and natural images, and demonstrate the superiority of our approach to the state-of-the-art low-rank NRSfM approaches.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134160425","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: 22

Registration of Point Clouds Based on the Ratio of Bidirectional Distances 基于双向距离比的点云配准

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.85

Jihua Zhu, Di Wang, Xiuxiu Bai, Huimin Lu, Congcong Jin, Zhongyu Li

引用次数: 12

Automatic 3D Car Model Alignment for Mixed Image-Based Rendering 基于混合图像渲染的自动3D汽车模型对齐

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.37

Rodrigo Ortiz Cayon, Abdelaziz Djelouah, Francisco Massa, Mathieu Aubry, G. Drettakis

引用次数: 9

Robust Feature-Preserving Denoising of 3D Point Clouds 三维点云的鲁棒特征保持去噪

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.17

Sk. Mohammadul Haque, V. Govindu

引用次数: 5

Absolute Pose and Structure from Motion for Surfaces of Revolution: Minimal Problems Using Apparent Contours 绝对姿态和结构从运动的旋转曲面:最小的问题使用表观轮廓

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.86

Cody J. Phillips, Kostas Daniilidis

{"title":"Absolute Pose and Structure from Motion for Surfaces of Revolution: Minimal Problems Using Apparent Contours","authors":"Cody J. Phillips, Kostas Daniilidis","doi":"10.1109/3DV.2016.86","DOIUrl":"https://doi.org/10.1109/3DV.2016.86","url":null,"abstract":"The class of objects that can be represented by surfaces of revolution (SoRs) is highly prevalent in human work and living spaces. Due to their prevalence and convenient geometric properties, SoRs have been employed over the past thirty years for single-view camera calibration and pose estimation, and have been studied in terms of SoR object reconstruction and recognition. Such treatment has provided techniques for the automatic identification and classification of important SoR structures, such as apparent contours, cross sections, bitangent points, creases, and inflections. The presence of these structures are crucial to most SoR-based image metrology algorithms. This paper develops single-view and two-view pose recovery and reconstruction formulations that only require apparent contours, and no other SoR features.The primary objective of this paper is to present and experimentallyvalidate the minimal problems pertaining toSoR metrology from apparent contours. For a single view with a known reference model, this includes absolute pose recovery. For many views and no reference model this is extended to structure from motion (SfM). Assuming apparent contours as input that have been identified and segmented with reasonable accuracy, the minimal problems aredemonstrated to produce accurate SoR pose and shape results when used as part of a RANSAC-based hypothesis generation and evaluation pipeline.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130826788","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

Multi-View Inpainting for Image-Based Scene Editing and Rendering 多视图绘画基于图像的场景编辑和渲染

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.44

T. Thonat, Eli Shechtman, Sylvain Paris, G. Drettakis

引用次数: 26

Face Reconstruction on Mobile Devices Using a Height Map Shape Model and Fast Regularization 基于高度映射形状模型和快速正则化的移动设备人脸重建

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.59

Fabio Maninchedda, Christian Häne, Martin R. Oswald, M. Pollefeys

{"title":"Face Reconstruction on Mobile Devices Using a Height Map Shape Model and Fast Regularization","authors":"Fabio Maninchedda, Christian Häne, Martin R. Oswald, M. Pollefeys","doi":"10.1109/3DV.2016.59","DOIUrl":"https://doi.org/10.1109/3DV.2016.59","url":null,"abstract":"We present a system which is able to reconstruct human faces on mobile devices with only on-device processing using the sensors which are typically built into a current commodity smart phone. Such technology can for example be used for facial authentication purposes or as a fast preview for further post-processing. Our method uses recently proposed techniques which compute depth maps by passive multi-view stereo directly on the device. We propose an efficient method which recovers the geometry of the face from the typically noisy point cloud. First, we show that we can safely restrict the reconstruction to a 2.5D height map representation. Therefore we then propose a novel low dimensional height map shape model for faces which can be fitted to the input data efficiently even on a mobile phone. In order to be able to represent instance specific shape details, such as moles, we augment the reconstruction from the shape model with a distance map which can be regularized efficiently. We thoroughly evaluate our approach on synthetic and real data, thereby we use both high resolution depth data acquired using high quality multi-view stereo and depth data directly computed on mobile phones.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124931951","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: 10

Fast Obstacle Detection Using Sparse Edge-Based Disparity Maps 基于稀疏边缘的视差图的快速障碍物检测

2016 Fourth International Conference on 3D Vision (3DV) Pub Date : 2016-10-01 DOI: 10.1109/3DV.2016.80

Dexmont Alejandro Peãa Carrillo, Alistair Sutherland

引用次数: 5