Applying Point Prompt Tuning with platform-specific conditioning to a PTv3 backbone improves multi-platform LiDAR segmentation on GOOSE/GOOSE-Ex validation data, with mIoU gains up to 22.59% relative to the PTv3 baseline.
The GOOSE Dataset for Perception in Unstructured Environments,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems
Applying Point Prompt Tuning with platform-specific conditioning to a PTv3 backbone improves multi-platform LiDAR segmentation on GOOSE/GOOSE-Ex validation data, with mIoU gains up to 22.59% relative to the PTv3 baseline.