CVPR-2018
1.CodeSlam:对单目slam算法的关键帧进行深度估计，使用网络架构对单目图像进行处理
2.MapNet：去中心化的环境建图，并且能够完成重定位，使用RNN网络
3.P2P-flyingcamera：飞行图像合成中的p2p问题求解
4.Unknown-Principal-Point：主点位置未知的相机位姿估计
5.GeoNet：使用无监督学习的方法估计单目图像深度，计算单目视频中的光流和相机位姿
6.Nonminimal-Global-Optimal-Solution：Non-Minimal相对位姿问题的可证明的全局最优解
7.HybridPoseEstimation：2D-3D匹配和2D-2D匹配的混合位姿估计方法
8.PolarimetricSLAM：利用Polarimetric相机的稠密单目SLAM算法。
9.ICE-BA：针对VI-SLAM的一种BA算法。
10.Geometric-MapNet：自监督，利用图像几何约束的建图工作,用于相机定位
11.SingleCameraLocalization：给定3D建筑物和单帧图像，预测相机拍摄时所在的位置，CNN
12.DeLS-3D:多传感器融合算法，GPS/IMU给定粗略的相机位姿，投影出一个3D语义地图，label map和图像送到CNN网络得到粗略的Pose，再利用RNN算法得到精确的Pose，最后把Pose和图像送到segment CNN生成像素级别的语义分割
13.Semantic-Localization：一种生成式模型用于描述子学习，可以表征3D几何信息和语义信息，用于视觉定位
14.inLoc:稠密的特征提取和匹配方法，用于室内场景的相机定位
15.BenchmarkLocalization：Benchmark，用于相机定位，同一场景的条件有巨大变化
references
[1]Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM-Learning a Compact, Optimisable Representation for Dense Visual SLAM[J]. arXiv preprint arXiv:1804.00874, 2018.
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[3]Lan Z, Hsu D, Lee G H. Solving the Perspective-2-Point Problem for Flying-Camera Photo Composition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 4588-4596.
[4]Larsson V, Kukelova Z, Zheng Y. Camera Pose Estimation With Unknown Principal Point[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2984-2992.
[5]Yin Z, Shi J. GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018, 2.
[6]Briales J, Kneip L, Gonzalez-Jimenez J. A Certifiably Globally Optimal Solution to the Non-Minimal Relative Pose Problem[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 145-154.
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[8]Yang L, Tan F, Li A, et al. Polarimetric Dense Monocular SLAM[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3857-3866.
[9]Liu H, Chen M, Zhang G, et al. ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.
[10]Brahmbhatt S, Gu J, Kim K, et al. Geometry-Aware Learning of Maps for Camera Localization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2616-2625.
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[12]Wang P, Yang R, Cao B, et al. Dels-3d: Deep localization and segmentation with a 3d semantic map[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 5860-5869.
[13]Schönberger J L, Pollefeys M, Geiger A, et al. Semantic Visual Localization[J]. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS), 2018.
[14]Taira H, Okutomi M, Sattler T, et al. InLoc: Indoor Visual Localization with Dense Matching and View Synthesis[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 7199-7209.
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CVPR-2017
1.NID-SLAM：使用Normalised information distance度量的单目slam算法，避免了photometric度量的诸如光照、天气、环境结构变化带来的影响。
2.CNN-SLAM:CNN预测深度，并且和测量深度相融合的单目直接法slam
3.MistyThreePoints：水下图像，使用三个点求解相机相对位姿
4.RegressionForests：使用一个预训练的regression forests做camera relocalization。
5.RankConstraintFMatrix:Multi-view中秩约束的基础矩阵，并将其应用到camera location恢复。
6.GeometricLossLocalization：深度学习，利用几何冲投影误差的损失函数，用于camera pose regression
7.EventVIO:使用EKF框架，event相机的VIO算法
8.3D-ModelsAreNotNecessary：相机的定位不依赖高精度的3D模型，只需要图像数据库和局部的三维重建即可实现visual localization。
9.ContextualFeatureReweight:图像的Geo-localization，知道图像拍摄的地理位置（和位姿不一样），使用contextual reweight network预测图像中的哪个部分更重要。
10.Cross-View-ImageMatching：不同视角的图像匹配，用于image geo-localization。
11.TwoPointsLocalization:在一个3D场景中定位一个query image，2D-3D的匹配问题，两对对应点可以将相机的位置约束在一个圆环面上，增加一个direction of triangulation就可以近似得到相机的位置。
12.DSAC：camera localization，将RANSAC中的deterministic hypothesis selection替换为 probabilistic selection，这种方法被称为RANSAC的可微副本，应用该方法解决camera localization的问题。
references
[1]Pascoe G, Maddern W, Tanner M, et al. NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance[C]//Conference on Computer Vision and Pattern Recognition. 2017.
[2]Tateno K, Tombari F, Laina I, et al. CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017, 2.
[3]Palmér T, Astrom K, Frahm J M. The Misty Three Point Algorithm for Relative Pose[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 2786-2794.
[4]Cavallari T, Golodetz S, Lord N A, et al. On-the-fly adaptation of regression forests for online camera relocalisation[C]//CVPR. 2017, 2(4): 7.
[5]Sengupta S, Amir T, Galun M, et al. A New Rank Constraint on Multi-view Fundamental Matrices, and Its Application to Camera Location Recovery[C]//CVPR. 2017: 2413-2421.
[6]Kendall A, Cipolla R. Geometric loss functions for camera pose regression with deep learning[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 6555-6564.
[7]Zhu A Z, Atanasov N, Daniilidis K. Event-Based Visual Inertial Odometry[C]//CVPR. 2017: 5816-5824.
[8]Sattler T, Torii A, Sivic J, et al. Are large-scale 3D models really necessary for accurate visual localization?[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 6175-6184.
[9]Kim H J, Dunn E, Frahm J M. Learned Contextual Feature Reweighting for Image Geo-Localization[C]//CVPR. 2017: 3251-3260.
[10]Tian Y, Chen C, Shah M. Cross-view image matching for geo-localization in urban environments[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017: 1998-2006.
[11]Camposeco F, Sattler T, Cohen A, et al. Toroidal constraints for two-point localization under high outlier ratios[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 6700-6708.
[12]Brachmann E, Krull A, Nowozin S, et al. DSAC—Differentiable RANSAC for camera localization[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 2492-2500.
ICCV-2017
1.StereoDSO:双目相机的DSO算法
2.VO-PPA：像素处理器阵列上的VO
3.ScaleRecovery：利用deep Convolutional Neural Fields估计深度，并实现单目VO中的尺度恢复
4.SpaceTimeLocalizationMapping：对动态场景进行建图，引入了一个4D结构的生成概率模型来说明位置、空间和时间范围
5.Global2D-3DMatching：大场景3D地图中，用于相机定位的全局2D-3D匹配算法，在3D地图上构建了Markov网络，考虑了不仅仅时视觉相似性，同时还有全局一致性
6.InlierSetMaximization：单帧图像与3D场景的对应，提出了一个全局最优的inlier set cardinality maximisation联合估计最优相机位姿和最优的点对应。另外还利用了BnB搜索6D空间，这个和发表在T-PAMI上的Go-ICP算法类似。
7.DistributedOptimizationBA：大场景下的SfM中的分布式BA算法，从经典的ADMM优化算法中推导一个分布式的formulation。
8.P4PfrMinimalSolvers：一个P4Pfr的minimal solvers。
9.EdgeSLAM：检测图像中的Edge点并使用光流法跟踪，并利用three views的几何关系去优化点的对应
10.DepthPredictions：CNN深度预测，sparse 点跟踪的单目slam，使用3D mesh的地图表示方法使得尽可能刚性地更新变换。
11.IntegerArithmetic：在EKF SfM的基础上提出了平方根滤波算法，能够用整数运算替代浮点型运算。
references
[1]Wang R, Schworer M, Cremers D. Stereo dso: Large-scale direct sparse visual odometry with stereo cameras[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3903-3911.
[2]Bose L, Chen J, Carey S J, et al. Visual Odometry for Pixel Processor Arrays[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 4604-4612.
[3]Yin X, Wang X, Du X, et al. Scale Recovery for Monocular Visual Odometry Using Depth Estimated with Deep Convolutional Neural Fields[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 5870-5878.
[4]Lee M, Fowlkes C C. Space-Time Localization and Mapping[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3912-3921.
[5]Liu L, Li H, Dai Y. Efficient global 2d-3d matching for camera localization in a large-scale 3d map[C]//Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017: 2391-2400.
[6]Campbell D, Petersson L, Kneip L, et al. Globally-optimal inlier set maximisation for simultaneous camera pose and feature correspondence[C]//The IEEE International Conference on Computer Vision (ICCV). 2017, 1(3).
[7]Zhang R, Zhu S, Fang T, et al. Distributed very large scale bundle adjustment by global camera consensus[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 29-38.
[8]Larsson V, Kukelova Z, Zheng Y. Making minimal solvers for absolute pose estimation compact and robust[C]//2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017: 2335-2343.
[9]Maity S, Saha A, Bhowmick B. Edge SLAM: Edge Points Based Monocular Visual SLAM[C]//ICCV Workshops. 2017: 2408-2417.
[10]Mukasa T, Xu J, Bjorn S. 3D Scene Mesh from CNN Depth Predictions and Sparse Monocular SLAM[C]//Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on. IEEE, 2017: 912-919.
[11]Ahuja N A, Subedar M, Tickoo O, et al. A Factorization Approach for Enabling Structure-from-Motion/SLAM Using Integer Arithmetic[C]//Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on. IEEE, 2017: 554-562.
ECCV-2018
1.SemanticMatch：visual localization的问题，用语义信息来匹配
2.EventSemi-Dense：双目Event相机的半稠密3D重建
3.TimeOffset：建模变化的camera-IMU时间偏移，提出了基于优化的VIO算法
4.GoodLineCutting：提出了一种提取most-informative子线段的方法，主要研究在基于之间的最小二乘问题中，line cutting对位姿估计中信息增益的影响
5.Shape-from-Template：Rolling Shutter的畸变可以被解释为Global shutter相机采集模板的虚拟畸变。类似于Shape-from-Template,提出使用局部微分约束
6.PointsLinesMinimalSolution：使用点和线的minimal solver问题，提出了闭合形式的解
7.VSO：使用语义信息实现medium-term的点的tracking。帧与帧之间的trackin是short-term，loop closure是long-term。
8.RollingShutterDSO:Rolling shutter 相机的DSO算法
9.DeepTAM：基于关键帧的稠密相机跟踪和深度map估计都是通过学习的方式得到的，利用学习的方法估计当前图像和合成的视点之间的小的位姿增量，生成大量的位姿假设会得到更精确的预测；地图构建过程使用了学习的方法进行深度预测
10.DeepDSO：深度学习的方法depth prediction，DSO算法
11.ADVIO：一个可靠的VIO数据集
12.LinearRGBDSLAM：基于线性EKF框架的RGBD slam算法，旋转是非线性的，利用曼哈顿世界的structural regularity可以实现线性化
references
[1]Toft C, Stenborg E, Hammarstrand L, et al. Semantic match consistency for long-term visual localization[C]//European Conference on Computer Vision. Springer, Cham, 2018: 391-408.
[2]Zhou Y, Gallego G, Rebecq H, et al. Semi-dense 3d reconstruction with a stereo event camera[C]//European Conference on Computer Vision. Springer, Cham, 2018: 242-258.
[3]Ling Y, Bao L, Jie Z, et al. Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 484-500.
[4]Zhao Y, Vela P A. Good Line Cutting: Towards Accurate Pose Tracking of Line-Assisted VO/VSLAM[C]//European Conference on Computer Vision. Springer, Cham, 2018: 527-543.
[5]Lao Y, Ait-Aider O, Bartoli A. Rolling Shutter Pose and Ego-motion Estimation using Shape-from-Template[C]//European Conference on Computer Vision. Springer, Cham, 2018: 477-492.
[6]Miraldo P, Dias T, Ramalingam S. A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines[C]//European Conference on Computer Vision. Springer, Cham, 2018: 490-507.
[7]Lianos K N, Schonberger J L, Pollefeys M, et al. VSO: Visual Semantic Odometry[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 234-250.
[8]Schubert D, Demmel N, Usenko V, et al. Direct Sparse Odometry with Rolling Shutter[C]//European Conference on Computer Vision. Springer, Cham, 2018: 699-714.
[9]Zhou H, Ummenhofer B, Brox T. Deeptam: Deep tracking and mapping[C]//European Conference on Computer Vision. Springer, Cham, 2018: 851-868.
[10]Yang N, Wang R, Stückler J, et al. Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry[C]//European Conference on Computer Vision. Springer, Cham, 2018: 835-852.
[11]Cortés S, Solin A, Rahtu E, et al. ADVIO: An authentic dataset for visual-inertial odometry[C]//European Conference on Computer Vision. Springer, Cham, 2018: 425-440.
[12]Kim P, Coltin B, Jin Kim H. Linear RGB-D SLAM for Planar Environments[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 333-348.