3 citations · 29 across the 66 of their papers we have counts for
5 papers · 1 filter
Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
Rui Huang, Songyou Peng, Ayca Takmaz +5
Current 3D scene segmentation methods are heavily dependent on manually annotated 3D training datasets. Such manual annotations are labor-intensive, and often lack fine-grained det…
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Silvan Weder, Francis Engelmann, Johannes L. Schönberger +3
We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly appli…
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Silvan Weder, Hermann Blum, Francis Engelmann +1
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, wit…
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation
Yu Zheng, Guangming Wang, Jiuming Liu +2
LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Recently, many works project the point cloud…
SNI-SLAM: Semantic Neural Implicit SLAM
Siting Zhu, Guangming Wang, Hermann Blum +4
We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, a…