Toward a foundation model for forest point clouds

arXiv:2609.24787 · cs.CV, cs.RO · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/prs-eth/ForPT

Terminology

• Fine-Tuning — 80× in this paper · explained in 5 episodes
• Point Clouds — 71× in this paper · explained in 2 episodes
• Foundation Model — 60× in this paper · explained in 3 episodes
• Linear Probing — 46× in this paper · explained in 2 episodes
• Instance Segmentation — 27× in this paper · explained in 3 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Downstream Tasks — 14× in this paper · explained in 2 episodes
• Self-Supervised Learning — 13× in this paper · explained in 8 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 10× in this paper
• Distillation — 10× in this paper · explained in 2 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Transfer Learning — 9× in this paper · explained in 10 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Self-Distillation — 5× in this paper · explained in 7 episodes
• Benchmarking — 4× in this paper · explained in 4 episodes
• Transferability — 4× in this paper · explained in 4 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• In International Conference on Computer Vision — 3× in this paper
• Aperture — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• In International Conference on Machine Learning — 2× in this paper
• International Conference on Learning Representations — 2× in this paper
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Layer Normalization
• SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests

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