IROS-2020-point-cloud-analysis

IROS 2020 papers focusing on point cloud analysis

• PC-NBV: A Point Cloud Based Deep Network for Efficient Next Best View Planning.

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• Acquiring Mechanical Knowledge from 3D Point Clouds.

• Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation.

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• Voxel-Based Representation Learning for Place Recognition Based on 3D Point Clouds.

• Point Cloud Completion by Learning Shape Priors.

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• Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images.

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• Learning Continuous Object Representations from Point Cloud Data.

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• Kinematic Multibody Model Generation of Deformable Linear Objects from Point Clouds.

• Real-Time Spatio-Temporal LiDAR Point Cloud Compression.

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• GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles.

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• End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences.

• Real-time detection of broccoli crops in 3D point clouds for autonomous robotic harvesting.

• Factor Graph based 3D Multi-Object Tracking in Point Clouds.

• Physical Human-Robot Interaction with Real Active Surfaces using Haptic Rendering on Point Clouds.

• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation.

• Seed: A Segmentation-Based Egocentric 3D Point Cloud Descriptor for Loop Closure Detection.

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• GRIF Net: Gated Region of Interest Fusion Network for Robust 3D Object Detection from Radar Point Cloud and Monocular Image .

• A Point Cloud Registration Pipeline using Gaussian Process Regression for Bathymetric SLAM.

• PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation.

• A Novel Coding Architecture for LiDAR Point Cloud Sequence.

• Point Cloud Projective Analysis for Part-based Grasp Planning.

• 3D Instance Embedding Learning with a Structure-Aware Loss Function for Point Cloud Segmentation.

• 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation.

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• Robust RL-Based Map-less Local Planning: Using 2D Point Clouds as Observations.

• ECG: Edge-aware Point Cloud Completion with Graph Convolution.

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• SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving.

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