Self-supervised pretraining of Point-M2AE on ShapeNet-55 plus 2,400 tree point clouds improves cross-site leaf-wood segmentation and halves QSM-derived volume estimation error versus algorithmic baselines.
Buttresses were modelled as triangular plates with V = 1 2 ๐ฟ โ ๐ โ ๐ป from the recorded length, width, and height
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Self-supervised pretraining of Point-M2AE on ShapeNet-55 plus 2,400 tree point clouds improves cross-site leaf-wood segmentation and halves QSM-derived volume estimation error versus algorithmic baselines.