A task-specific iterative framework for weakly supervised 4D radar scene flow estimation uses instance-aware self-supervised losses from 2D tracking/segmentation and a rigid static loss from odometry to outperform LiDAR-dependent cross-modal and fully supervised methods on the VoD dataset.
Additional Experiments and Analysis C.1
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
A task-specific iterative framework for weakly supervised 4D radar scene flow estimation uses instance-aware self-supervised losses from 2D tracking/segmentation and a rigid static loss from odometry to outperform LiDAR-dependent cross-modal and fully supervised methods on the VoD dataset.