PRISM-3D
PRISM-3D
A forest segmentation workflow inspired by the principles of biological flow and human vision
PRISM-3D is a collaborative initiative between SPECLab GatorEye and CSER, supported by funding from ARRI, SPECLab GatorEye, and additional sources.
Principal Investigators: Broadbent, E., Almeyda Zambrano, A.M., Medley, P., and Drake, J.
PRISM-3D represents a top-tier system for unsupervised point cloud segmentation in forest environments. The framework integrates biologically inspired flow dynamics and principles of human visual perception to improve structural delineation within complex forest point clouds.
Project webpage: https://www.speclab.org/prism-3d
Web application (beta): https://prism-3d.fly.dev/
Beta Access Notice
All workflows are currently under active development and ongoing integration with additional forest types and varying point cloud acquisition parameters, including density and structural variability.
Citation
Almeyda Zambrano, A.M., Broadbent, E. N., Medley, P., and Drake, J. PRISM-3D: Web-based forest segmentation workflows inspired by the principles of biological flow and human vision. Accessed [Version] at https://www.speclab.org/prism-3d on [Date].
License
Beta preview use only. Commercial use is not permitted. Duplication, redistribution, or reverse engineering of the system is prohibited.
FLOW - outputs (LAS fields)
Versions
v15.1 - 03/01/26 - Improvements to web app interface processing capabilities.
v3.1 - 02/25/26 - Initial beta release of web app.
v1.0 - 08/15/25 - Initial framework (C++).
v15.1 - 03/01/26 - Improvements to web app interface processing capabilities.
v3.1 - 02/25/26 - Initial beta release of web app.
v1.0 - 08/15/25 - Initial framework (C++).
Methods (detailed):
PRISM 3D segments individual trees from Lidar-derived point clouds using principles and inspiration of biotic flow mimicry and human vision — simulating how water and nutrients move through tree structure to identify trunks, branches, and crowns. No training data required. Works across forest types from open woodland to dense multi-layer canopy.
Multiple Independent Workflows
Flow
Voxel-based biotic flow mimicry — simulates downward/upward biological flow through 3D voxel space, then Dijkstra expansion from detected seeds. Primary segmentation workflow.
Vision V3
Vision-based workflow for standalone automated point cloud 3D profiling and segmentation. Supportive secondary workflow.
Connectivity
Full per-point tree segmentation via downward flow paths — every point flows to ground via the cheapest KNN path; ground terminals are clustered into trees; every point receives a tree ID. Developed to enhance segmentation of small under-mid story trees. Supportive secondary workflow.
Each workflow runs independently on an input point cloud to predict tree segments. Flow is the primary production workflow (78% accuracy, 0.70 F1 on TEST). Vision V3 is the recommended standalone vision alternative (71.5% accuracy, 0.64 F1 on TEST. Connectivity is a full per-point tree segmentation workflow that traces downward flow paths to the ground.
Key Features
Accuracy
Benchmark assessments against global standard "FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees", available at: https://zenodo.org/records/8287792
Evaluated on held-out TEST datasets (not used during development):
Accuracy = fraction of tree points assigned to the correct tree (after optimal Hungarian matching). Large, well-separated trees are segmented near-perfectly; dense multi-layer canopy with overlapping crowns is the primary challenge.
Macro F1 = average per-tree F1, treating every tree equally regardless of size. Dragged down by small understory trees that are harder to detect.
FLOW - Biology-inspired (Standalone)
Dataset, Forest Type, Accuracy, Macro F1
CULS, Deciduous broadleaf, 91%, 0.8820
NIBIO, Boreal conifer, 81%, 0.6637
RMIT, Open eucalyptus woodland, 65%, 0.5464
SCION (31), Plantation conifer, 86%, 0.7925
SCION (61), Plantation conifer, 84%, 0.8118
TUWIEN, Dense mixed forest, 61%, 0.5035
Average, 78%, 0.70
Vision V3 — Profile-Guided (Standalone)Fully automatic standalone vision segmentation with automated point cloud profiling. No internet, no cloud API required.
Dataset, Forest Type, Accuracy, Macro F1
CULS, Deciduous broadleaf, 85.8%, 0.894
NIBIO, Boreal conifer, 80.6%, 0.517
RMIT, Open eucalyptus, 59.5%, 0.511
SCION (31), Plantation conifer, 77.8%, 0.741
SCION (61), Plantation conifer, 78.9%, 0.807
TUWIEN, Dense mixed, 46.7%, 0.396
Average, 71.5%, 0.644
PRISM 3D segments individual trees from Lidar-derived point clouds using principles and inspiration of biotic flow mimicry and human vision — simulating how water and nutrients move through tree structure to identify trunks, branches, and crowns. No training data required. Works across forest types from open woodland to dense multi-layer canopy.
Multiple Independent Workflows
Flow
Voxel-based biotic flow mimicry — simulates downward/upward biological flow through 3D voxel space, then Dijkstra expansion from detected seeds. Primary segmentation workflow.
Vision V3
Vision-based workflow for standalone automated point cloud 3D profiling and segmentation. Supportive secondary workflow.
Connectivity
Full per-point tree segmentation via downward flow paths — every point flows to ground via the cheapest KNN path; ground terminals are clustered into trees; every point receives a tree ID. Developed to enhance segmentation of small under-mid story trees. Supportive secondary workflow.
Each workflow runs independently on an input point cloud to predict tree segments. Flow is the primary production workflow (78% accuracy, 0.70 F1 on TEST). Vision V3 is the recommended standalone vision alternative (71.5% accuracy, 0.64 F1 on TEST. Connectivity is a full per-point tree segmentation workflow that traces downward flow paths to the ground.
Key Features
- Online web app for processing of LAS clouds up to 500 MB (beta access available)
- Fully automatic — drop in a .las file, get segmented trees out
- Dynamic voxel resolution — voxel size adapts to input point density (0.35 m for dense clouds ≥ 10 pts/m², up to 1.0 m for sparse clouds ≤ 1 pt/m²) via log-linear interpolation; all resolution-dependent parameters scale automatically
- Adaptive density profiling — vertical profiling characterizes point distribution across height strata; horizontal variability profiling measures spatial uniformity across the XY plane. Together, they produce 7 adaptive weights that steer the pipeline for point clouds with sparse strata or patchy coverage
- Hybrid trunk detection — voxel-based flow convergence + point-scale two-pass convergence & chain union for 99%+ trunk detection
- Flow workflow — simulates downward/upward biolotical flows through 3D voxel space
- Multi-phase pipeline — 5 phases (flow, detection, core building, crown assignment, crown separation) plus hierarchical post-processing
- Per-tree metrics — DBH, tree height, crown width, crown base height, live crown ratio (*beta dev)
- Structural classification — every point labeled as ground, trunk, branch, or crown
- Multi-core parallel — embarrassingly-parallel phases (point assignment, smoothing) and per-column flow processing distributed across all CPU cores via ProcessPoolExecutor; hybrid trunk detection runs concurrently in a background thread; correctness-critical Dijkstra phases (core building, crown assignment) stay sequential; --verify-parallel proves byte-identical output
Accuracy
Benchmark assessments against global standard "FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees", available at: https://zenodo.org/records/8287792
Evaluated on held-out TEST datasets (not used during development):
Accuracy = fraction of tree points assigned to the correct tree (after optimal Hungarian matching). Large, well-separated trees are segmented near-perfectly; dense multi-layer canopy with overlapping crowns is the primary challenge.
Macro F1 = average per-tree F1, treating every tree equally regardless of size. Dragged down by small understory trees that are harder to detect.
FLOW - Biology-inspired (Standalone)
Dataset, Forest Type, Accuracy, Macro F1
CULS, Deciduous broadleaf, 91%, 0.8820
NIBIO, Boreal conifer, 81%, 0.6637
RMIT, Open eucalyptus woodland, 65%, 0.5464
SCION (31), Plantation conifer, 86%, 0.7925
SCION (61), Plantation conifer, 84%, 0.8118
TUWIEN, Dense mixed forest, 61%, 0.5035
Average, 78%, 0.70
Vision V3 — Profile-Guided (Standalone)Fully automatic standalone vision segmentation with automated point cloud profiling. No internet, no cloud API required.
Dataset, Forest Type, Accuracy, Macro F1
CULS, Deciduous broadleaf, 85.8%, 0.894
NIBIO, Boreal conifer, 80.6%, 0.517
RMIT, Open eucalyptus, 59.5%, 0.511
SCION (31), Plantation conifer, 77.8%, 0.741
SCION (61), Plantation conifer, 78.9%, 0.807
TUWIEN, Dense mixed, 46.7%, 0.396
Average, 71.5%, 0.644
