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Found 10 papers, 7 papers with code
Learnable Chamfer Distance for Point Cloud Reconstruction
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules.
Camera-based 3D Semantic Scene Completion with Sparse Guidance Network
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving.
OverlapNetVLAD: A Coarse-to-Fine Framework for LiDAR-based Place Recognition
Place recognition is a challenging yet crucial task in robotics.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
Dynamically Stable Poincaré Embeddings for Neural Manifolds
As a result, we prove that, if initial metrics have an $L^2$-norm perturbation which deviates from the Hyperbolic metric on the Poincar\'e ball, the scaled Ricci-DeTurck flow of such metrics smoothly and exponentially converges to the Hyperbolic metric.
SSC: Semantic Scan Context for Large-Scale Place Recognition
We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance.
Ranked #1 on
Visual Place Recognition
on KITTI
SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
Semantic Graph Based Place Recognition for 3D Point Clouds
First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.
