Search Results for author:
Found 7 papers, 5 papers with code
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i. e., non-i. i. d.)
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified.
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points.
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition
In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual place recognition, which can obtain good performance and reduce computation memory and inference time.
Superpoint Network for Point Cloud Oversegmentation
Specifically, in our clustering network, we first jointly learn a soft point-superpoint association map from the coordinate and feature spaces of point clouds, where each point is assigned to the superpoint with a learned weight.
Pyramid Point Cloud Transformer for Large-Scale Place Recognition
In order to obtain discriminative global descriptors, we construct a pyramid VLAD module to aggregate the multi-scale feature maps of point clouds into the global descriptors.
Ranked #3 on
3D Place Recognition
on Oxford RobotCar Dataset
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation
In this paper, we propose a cascaded non-local neural network for point cloud segmentation.
