1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
Emergent 3D Instance Segmentation from Self-Supervised Point Transformers
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
Unsupervised 3D instance segmentation of outdoor LiDAR scans has traditionally relied on handcrafted geometric priors such as density-based clustering, motion cues, or projected 2D…
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground se…
UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Ted Lentsch, Holger Caesar, Dariu M. Gavrila
Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for l…
Offline Tracking with Object Permanence
Xianzhong Liu, Holger Caesar
To reduce the expensive labor cost for manual labeling autonomous driving datasets, an alternative is to automatically label the datasets using an offline perception system. Howeve…