Self supervised model improves forest 3D point cloud analysis
Toward a foundation model for forest point clouds
Computer Vision and Pattern RecognitionRobotics
Summary
Measuring forest characteristics from 3D scans usually requires many specific AI models trained for each type of forest and sensor, which is costly. The authors tested if one general model, trained on a large variety of forest scans without labels, can learn useful features that work for many tasks. Their approach speeds up learning and works better when there are few labeled examples. They also found that telling trees apart as individual objects, rather than just recognizing forest types, remains a key challenge for a universal forest AI model.
What this means in practice
- •For forest management teams: Use a single pretrained model to estimate tree species and age from various 3D scans with fewer labeled examples required.
- •For drone survey operators: Analyze forest scans collected by UAVs with a unified AI model that adapts well to different forest types and sensors.
Authors
Yuanwen Yue, Stefano Puliti, Damien Robert, Atakan Topaloğlu, Binbin Xiang, Maciej Wielgosz, Jan Dirk Wegner, Rasmus Astrup, Christian Rupprecht, Konrad Schindler
Abstract
Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models are typically specialized to a single task, sensor, and forest type, making adaptation expensive in terms of annotations, computation, and expertise. We ask whether a single pretrained model can instead learn transferable representations across diverse forest inventory settings. Inspired by recent developments in language modelling and computer vision, we take a step toward a foundation model (FM) for 3D forestry. Using LitePT as backbone, we first establish a strong supervised baseline that sets a new state of the art on forest semantic and instance segmentation, tree species classification, and age regression benchmarks. We then curate a large-scale unlabelled corpus spanning airborne, UAV, and mobile laser scanning across diverse forest ecosystems, and pretrain the same backbone using self-supervised learning. We systematically evaluate representation learning strategies by comparing training from scratch, supervised pretraining, and self-supervised pretraining across four representative forestry tasks, under varying annotation budgets. Compared with training from scratch, self-supervised pretraining accelerates model convergence and consistently improves performance when annotations are scarce. Compared with task-specific supervised pretraining, self-supervised pretraining yields more transferable representations across downstream forestry tasks. These findings identify the practical regime in which pretrained representations are most valuable and suggest that instance discrimination, rather than forest semantics, is the main remaining obstacle to a general-purpose 3D forest foundation model. Code and models are available at: https://github.com/prs-eth/ForPT.