Three-dimensional point cloud semantic segmentation for cultural heritage: a comprehensive review
S Yang, M Hou, S Li - Remote Sensing, 2023 - mdpi.com
In the cultural heritage field, point clouds, as important raw data of geomatics, are not only
three-dimensional (3D) spatial presentations of 3D objects but they also have the potential …
three-dimensional (3D) spatial presentations of 3D objects but they also have the potential …
Lasermix for semi-supervised lidar semantic segmentation
Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-
supervised learning methods. In this work, we study the underexplored semi-supervised …
supervised learning methods. In this work, we study the underexplored semi-supervised …
[HTML][HTML] WSPointNet: A multi-branch weakly supervised learning network for semantic segmentation of large-scale mobile laser scanning point clouds
Semantic segmentation of large-scale mobile laser scanning (MLS) point clouds is essential
for urban scene understanding. However, most of the existing semantic segmentation …
for urban scene understanding. However, most of the existing semantic segmentation …
Multi-modal data-efficient 3d scene understanding for autonomous driving
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …
Growsp: Unsupervised semantic segmentation of 3d point clouds
We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing
methods which primarily rely on a large amount of human annotations for training neural …
methods which primarily rely on a large amount of human annotations for training neural …
Partslip: Low-shot part segmentation for 3d point clouds via pretrained image-language models
Generalizable 3D part segmentation is important but challenging in vision and robotics.
Training deep models via conventional supervised methods requires large-scale 3D …
Training deep models via conventional supervised methods requires large-scale 3D …
Scribble-supervised lidar semantic segmentation
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep
up with the ever growing volume of data. While current literature focuses on fully-supervised …
up with the ever growing volume of data. While current literature focuses on fully-supervised …