【泡泡汇总】CVPR2019 SLAM Paperlist

 

CVPR2019 六月在美国召开，我们对SLAM相关的会议论文进行了整理分类。

主要分为以下几类：

    1.匹配

    2.匹配-深度学习

    3.三维重建

    4.三维重建-深度学习

    5.定位

    6.定位-深度学习

    7.跟踪

    8.跟踪-深度学习

    9.深度估计

    10.深度估计-深度学习

    11.标定-深度学习

    12.目标检测

    13.目标检测-深度学习

    14.自动驾驶

    15.其他

各类的论文如下：

匹配：

1. SDRSAC: Semidefinite-Based Randomized Approach for Robust  Point Cloud Registration without Correspondences

2. NM-Net: Mining Reliable Neighbors for Robust Feature  Correspondences

3. The Perfect Match: 3D Point Cloud Matching with Smoothed  Densities
   

匹配-深度学习：

1. GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

2. Guided Stereo Matching

3. Multi-Level Context Ultra-Aggregation for Stereo Matching

4. PointNetLK: Robust & Efficient Point Cloud Registration  using PointNet

三维重建：

1. Coordinate-Free Carlsson-Weinshall Duality and Relative  Multi-View Geometry

2. PlaneRCNN: 3D Plane Detection and Reconstruction from a Single  View

3. Single-Image Piece-wise Planar 3D Reconstruction via  Associative Embedding

4. GPSfM: Global Projective SFM Using Algebraic Constraints\\ on  Multi-View Fundamental Matrices

5. Privacy Preserving Image-based Localization

6. Visual Localization by Learning Objects-of-Interest Dense  Match Regression

7. Robust Point Cloud Reconstruction of Large-Scale Outdoor  Scenes

8. SceneCode: Monocular Dense Semantic Reconstruction using  Learned Encoded Scene Representations

三维重建-深度学习：

1. Revealing Scenes by Inverting Structure from Motion  Reconstructions

2. Deep Reinforcement Learning of Volume-guided Progressive View  Inpainting for 3D Point Scene Completion from a Single Depth Image

3. What Do Single-view 3D Reconstruction Networks Learn?

4. Learning View Priors for Single-view 3D Reconstruction

定位:

1. PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation

2. Hybrid Scene Compression for Visual Localization

3. The Alignment of the Spheres: Globally-Optimal Spherical  Mixture Alignment for Camera Pose Estimation

定位-深度学习：

1. Normalized Object Coordinate Space for Category-Level 6D  Object Pose and Size Estimation

2. Extreme Relative Pose Estimation for RGB-D Scans via Scene  Completion

3. Understanding the Limitations of CNN-based Absolute Camera  Pose Regression

4. DeepLiDAR: Deep Surface Normal Guided Depth Prediction for  Outdoor Scene from Sparse LiDAR Data and Single Color Image

5. DenseFusion: 6D Object Pose Estimation by Iterative Dense  Fusion

6. Segmentation-driven 6D Object Pose Estimation

7. PointFlowNet: Learning Representations for Rigid Motion  Estimation from Point Clouds

8. From Coarse to Fine: Robust Hierarchical Localization at Large  Scale
   

 

跟踪：

1. VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense  Feature Points

2. Motion estimation of non-holonomic ground vehicles from a  single feature correspondence measured over n views
   

跟踪-深度学习：

1. Unsupervised Event-based Learning of Optical Flow, Depth, and  Egomotion

2. SPLFlowNet: Sparse Permutohedral Lattice FlowNet for Scene  Flow Estimation on Large-scale Point Clouds

3. SIGNet: Semantic Instance Aided Unsupervised 3D Geometry  Perception

深度估计：

1. Recurrent MVSNet for High-resolution Multi-view Stereo Depth  Inference

2. Learning Single-Image Depth from Videos using Quality  Assessment Networks

3. Depth from a polarisation + RGB stereo pair

4. Monocular Depth Estimation Using Relative Depth Maps

5. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth  Estimation

6. CAM-Convs: Camera-Aware Multi-Scale Convolutions for  Single-View Depth Prediction
   

深度估计-深度学习：

1. Recurrent Neural Network for (Un-)supervised Learning of  Monocular Video Visual Odometry and Depth

2. Connecting the Dots: Learning Representations for Active  Monocular Depth Estimation

3. Learning Non-Volumetric Depth Fusion using Successive  Reprojections

4. Learning monocular depth estimation infusing traditional  stereo knowledge

标定-深度学习：

1. Deep Single Image Camera Calibration with Radial Distortion

目标检测：

1. PointRCNN: 3D Object Proposal Generation and Detection from  Point Cloud

目标检测-深度学习：

1. Deep Relational Reasoning Network for Monocular 3D Object  Detection

2. ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and  Metric Shape

自动驾驶：

1. DrivingStereo: A Large-Scale Dataset for Stereo Matching in  Autonomous Driving Scenarios

2. GS3D: An Efficient 3D Object Detection Framework for  Autonomous Driving

3. ApolloCar3D: A Large 3D Car Instance Understanding Benchmark  for Autonomous Driving

4. Stereo R-CNN based 3D Object Detection for Autonomous Driving

5. Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in  3D Object Detection for Autonomous Driving

6. Rules of the Road: Predicting Driving Behavior with a  Convolutional Model of Semantic Interactions

其他：

1. BAD SLAM: Bundle  Adjusted Direct RGB-D SLAM

2. Modeling Local Geometric Structure of 3D Point  Clouds using  Geo-CNN

3. Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

4. 3D Motion Decomposition for RGBD Future Dynamic Scene  Synthesis

5. RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

6. D2-Net: A Trainable CNN for Joint Description and Detection of  Local Features

7. LO-Net: Deep Real-time Lidar Odometry

8. Octree guided CNN with Spherical Kernels for 3D Point Clouds

9. DeepMapping:  Unsupervised Map Estimation From Multiple Point Clouds

10. FlowNet3D:  Learning Scene Flow in 3D Point Clouds

如有任何遗漏或错误，欢迎大家批评指正~

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