Through the Perspective of LiDAR: A Feature-Enriched and
  Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud
  Segmentation
Accurate semantic segmentation of terrestrial laser scanning (TLS) point
clouds is limited by costly manual annotation. We propose a semi-automated,
uncertainty-aware pipeline that integrates spherical projection, feature
enrichment, ensemble learning, and targeted annotation to reduce labeling
effort, while sustaining high accuracy. Our approach projects 3D points to a 2D
spherical grid, enriches pixels with multi-source features, and trains an
ensemble of segmentation networks to produce pseudo-labels and uncertainty
maps, the latter guiding annotation of ambiguous regions. The 2D outputs are
back-projected to 3D, yielding densely annotated point clouds supported by a
three-tier visualization suite (2D feature maps, 3D colorized point clouds, and
compact virtual spheres) for rapid triage and reviewer guidance. Using this
pipeline, we build Mangrove3D, a semantic segmentation TLS dataset for mangrove
forests. We further evaluate data efficiency and feature importance to address
two key questions: (1) how much annotated data are needed and (2) which
features matter most. Results show that performance saturates after ~12
annotated scans, geometric features contribute the most, and compact
nine-channel stacks capture nearly all discriminative power, with the mean
Intersection over Union (mIoU) plateauing at around 0.76. Finally, we confirm
the generalization of our feature-enrichment strategy through cross-dataset
tests on ForestSemantic and Semantic3D.
  Our contributions include: (i) a robust, uncertainty-aware TLS annotation
pipeline with visualization tools; (ii) the Mangrove3D dataset; and (iii)
empirical guidance on data efficiency and feature importance, thus enabling
scalable, high-quality segmentation of TLS point clouds for ecological
monitoring and beyond. The dataset and processing scripts are publicly
available at https://fz-rit.github.io/through-the-lidars-eye/.
68ed2ca6de1fee572713a5a2
Fei Zhang
claimed_verified
2025-10-14T07:31:34.328Z
67508b51a76bbbdfb18b2e86
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/IhIodixqMEm4pGx2qO2zm.png
Fei Zhang
user
fz-rit-hf
68ed2ca6de1fee572713a5a3
Rob Chancia
68ed2ca6de1fee572713a5a4
Josie Clapp
68ed2ca6de1fee572713a5a5
Amirhossein Hassanzadeh
68ed2ca6de1fee572713a5a6
Dimah Dera
68ed2ca6de1fee572713a5a7
Richard MacKenzie
68ed2ca6de1fee572713a5a8
Jan van Aardt
Through the Perspective of LiDAR: A Feature-Enriched and
  Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud
  Segmentation
Accurate semantic segmentation of terrestrial laser scanning (TLS) point
clouds is limited by costly manual annotation. We propose a semi-automated,
uncertainty-aware pipeline that integrates spherical projection, feature
enrichment, ensemble learning, and targeted annotation to reduce labeling
effort, while sustaining high accuracy. Our approach projects 3D points to a 2D
spherical grid, enriches pixels with multi-source features, and trains an
ensemble of segmentation networks to produce pseudo-labels and uncertainty
maps, the latter guiding annotation of ambiguous regions. The 2D outputs are
back-projected to 3D, yielding densely annotated point clouds supported by a
three-tier visualization suite (2D feature maps, 3D colorized point clouds, and
compact virtual spheres) for rapid triage and reviewer guidance. Using this
pipeline, we build Mangrove3D, a semantic segmentation TLS dataset for mangrove
forests. We further evaluate data efficiency and feature importance to address
two key questions: (1) how much annotated data are needed and (2) which
features matter most. Results show that performance saturates after ~12
annotated scans, geometric features contribute the most, and compact
nine-channel stacks capture nearly all discriminative power, with the mean
Intersection over Union (mIoU) plateauing at around 0.76. Finally, we confirm
the generalization of our feature-enrichment strategy through cross-dataset
tests on ForestSemantic and Semantic3D.
  Our contributions include: (i) a robust, uncertainty-aware TLS annotation
pipeline with visualization tools; (ii) the Mangrove3D dataset; and (iii)
empirical guidance on data efficiency and feature importance, thus enabling
scalable, high-quality segmentation of TLS point clouds for ecological
monitoring and beyond. The dataset and processing scripts are publicly
available at https://fz-rit.github.io/through-the-lidars-eye/.
A semi-automated pipeline using spherical projection, feature enrichment, and ensemble learning reduces manual annotation effort for TLS point cloud segmentation while maintaining high accuracy.
spherical projection feature enrichment ensemble learning pseudo-labels uncertainty maps back-projection three-tier visualization suite Mangrove3D ForestSemantic Semantic3D mean Intersection over Union (mIoU)