作者丨wanghy
编辑丨3D视觉工坊
CVPR 2021 全部论文已经放出，网址：https://openaccess.thecvf.com/CVPR2021?day=all。特总结了与SLAM方向相关的论文，供SLAMer参考。
点云配准：
·RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2 D -Tree Representation
·SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
·PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
·ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning
·Robust Point Cloud Registration Framework Based on Deep Graph Matching
·PREDATOR : Registration of 3D Point Clouds with Low Overlap
·DeepI2P: Image-to-Point Cloud Registration via Deep Classification
点云压缩：
·VoxelContext-Net: An Octree based Framework for Point Cloud Compression
点云上采样&&点云补全：
·Point Cloud Upsampling via Disentangled Refinement
·PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
·PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
·View-Guided Point Cloud Completion
·Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
·Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
·Variational Relational Point Completion Network
·Unsupervised 3D Shape Completion through GAN Inversion
定位与建图：
·Privacy Preserving Localization and Mapping from Uncalibrated Cameras
·Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation
·Large-scale Localization Datasets in Crowded Indoor Spaces
·Rotation-Only Bundle Adjustment
·Learning Camera Localization via Dense Scene Matching
·Uncertainty-Aware Camera Pose Estimation from Points and Lines
·DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency
·VS-Net: Voting with Segmentation for Visual Localization
·Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
·Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
里程计：
·Globally Optimal Relative Pose Estimation with Gravity Prior
·Spatiotemporal Registration for Event-based Visual Odometry
·PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
·Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
AR：
·Stay Positive: Non-Negative Image Synthesis for Augmented Reality
·HDR Environment Map Estimation for Real-Time Augmented Reality video
·VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization
·Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
·ANR: Articulated Neural Rendering for Virtual Avatars
·Toward Accurate and Realistic Outfits Visualization with Attention to Details
闭环&&场景识别：
·OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
·Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
·SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition
标定：
·Wide-Baseline Multi-Camera Calibration using Person Re-Identification
SFM:
·Efficient Initial Pose-graph Generation for Global SfM
·Deep Two-View Structure-from-Motion Revisited
光流：
·Learning Optical Flow from a Few Matches
·UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
·AutoFlow: Learning a Better Training Set for Optical Flow
·Learning optical flow from still images
场景流：
·Weakly Supervised Learning of Rigid 3D Scene Flow
·Self-Supervised Multi-Frame Monocular Scene Flow
·FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
·HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
·Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk
Reconstruction:
·Learning to Recover 3D Scene Shape from a Single Image（Finalist）
·NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video （Finalist）
·Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
·Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance
·Square Root Bundle Adjustment for Large-Scale Reconstruction
·Self-Supervised 3D Mesh Reconstruction from Single Images
·DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors
·Learning monocular 3D reconstruction of articulated categories from motion
·Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
·Plan2Scene: Converting Floorplans to 3D Scenes
·Deep Optimized Priors for 3D Shape Modeling and Reconstruction
depth:
·AdaBins: Depth Estimation Using Adaptive Bins
·Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion
·Depth from Camera Motion and Object Detection
·Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
·Towards Fast and Accurate Real-World Depth Super Resolution: Benchmark Dataset and Baseline
·Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
·Learnable Sparse Signal Superdensity for Guided Depth Estimation
·Depth Completion with Twin Surface Extrapolation at Occlusion boundaries
·Differentiable Diffusion for Dense Depth Estimation from Multi-view Images
·Robust Consistent Video Depth Estimation
·Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks
·Radar-Camera Pixel Depth Association for Depth Completion
·Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging
·Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
·Beyond Image to Depth: Improving Depth Prediction using Echoes
·SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation
·Single Image Depth Prediction with Wavelet Decomposition
·Self-supervised Learning of Depth Inference for Multi-view Stereo
·Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
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