ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds | Paper Citation Record

Observation bdab4ec7-52c3-417f-bb61-b21025e3c166 · outbound

This paper cites LAUTx - individual tree point clouds from aus- trian forest inventory plots, 2022.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds LAUTx - individual tree point clouds from aus- trian forest inventory plots, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.393608Z

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Observation 15fa0c03-280b-42d5-ae7b-5e0ef862c2ea · outbound

This paper cites Zamir, Helen Jiang, Ioan- nis Brilakis, Martin Fischer, and Silvio Savarese.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Zamir, Helen Jiang, Ioan- nis Brilakis, Martin Fischer, and Silvio Savarese

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.375010Z

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Observation 42c873eb-0cf7-447a-894b-83e6075acfc8 · outbound

This paper cites Joint 2D-3D-Semantic Data for Indoor Scene Understanding.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Joint 2D-3D-Semantic Data for Indoor Scene Understanding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T19:22:26.624573Z

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Observation 9ffc1709-1e5b-46fc-b792-8eec5b4b5ec8 · outbound

This paper cites Kershaw Jr., Laura S.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Kershaw Jr., Laura S

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.356144Z

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Observation 6df95c68-cefa-4a12-82eb-804b76d08e19 · outbound

This paper cites Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.339544Z

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Observation 26b1be2b-c804-40b2-b5cc-3fef01253d95 · outbound

This paper cites Se- manticKITTI: A dataset for semantic scene understanding of LiDAR sequences.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Se- manticKITTI: A dataset for semantic scene understanding of LiDAR sequences

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.322049Z

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Observation 7737b5b5-e33c-4a1e-9d09-672b164acca0 · outbound

This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.305661Z

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Observation 129e6b8a-80ad-424b-a81a-76c256eaebe8 · outbound

This paper cites Laser scan- ning reveals potential underestimation of biomass carbon in temperate forest.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Laser scan- ning reveals potential underestimation of biomass carbon in temperate forest

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.288107Z

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Observation 363d243a-c68f-4c5f-8b13-679fffb740d2 · outbound

This paper cites A two-stage approach for individual tree segmenta- tion from TLS point clouds.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds A two-stage approach for individual tree segmenta- tion from TLS point clouds

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.271307Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc53cb06-7c2f-4a40-9f9a-b055eb3d31c9 · outbound

This paper cites Qi Charles, Hao Su, Mo Kaichun, and Leonidas J.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Qi Charles, Hao Su, Mo Kaichun, and Leonidas J

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.253679Z

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Observation 542b83ff-4909-4e3b-8089-290886e762ad · outbound

This paper cites Hierarchical aggregation for 3D instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Hierarchical aggregation for 3D instance segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.236029Z

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Observation 2d09fdc3-7a33-4271-9533-3e9de0965d0b · outbound

This paper cites Individual tree crown segmentation directly from UA V-borne LiDAR data using the PointNet of deep learning.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Individual tree crown segmentation directly from UA V-borne LiDAR data using the PointNet of deep learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.219182Z

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Observation 527a995c-5a98-463f-829c-013293d933dd · outbound

This paper cites 4D spatio-temporal convnets: Minkowski convolutional neural networks.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 4D spatio-temporal convnets: Minkowski convolutional neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.200947Z

Source-reported events for the cited work

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Observation affc5abc-2cb8-4a71-a491-a69037044c1b · outbound

This paper cites Spconv: Spatially sparse convolu- tion library.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Spconv: Spatially sparse convolu- tion library

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.183976Z

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Observation ae14c696-eda7-46e4-9a2f-04f176598ee3 · outbound

This paper cites Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.167814Z

Source-reported events for the cited work

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Observation 2cc062d3-e7a5-45ca-985b-24e6193b820f · outbound

This paper cites an unresolved cited work.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:22:28.151132Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15b23554-dbb0-4b75-82b0-64f7514aa02d · outbound

This paper cites Se- mantic instance segmentation for autonomous driving.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Se- mantic instance segmentation for autonomous driving

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.135272Z

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Observation 83904aa5-6822-4263-b57f-eb28b27fb1c1 · outbound

This paper cites Improved block merging for 3D point cloud instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Improved block merging for 3D point cloud instance segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.119632Z

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Observation d67b343d-e949-4d1c-b7ea-c0e01b177cd8 · outbound

This paper cites 3D Bird’s-Eye-View instance segmenta- tion.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 3D Bird’s-Eye-View instance segmenta- tion

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.104016Z

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Observation 95177382-c6d3-42d3-a6aa-247ed645ef6a · outbound

This paper cites 3D-MPA: Multi proposal ag- gregation for 3D semantic instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 3D-MPA: Multi proposal ag- gregation for 3D semantic instance segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.087765Z

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Observation af0b2042-f804-41ad-9fbf-e2bded2300b4 · outbound

This paper cites 3-D mapping of a multi-layered mediterranean for- est using ALS data.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 3-D mapping of a multi-layered mediterranean for- est using ALS data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.071485Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e821bf35-ee5f-40d7-a4b9-36b2a388bb7b · outbound

This paper cites Occuseg: Occupancy-aware 3D instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Occuseg: Occupancy-aware 3D instance segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.055776Z

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Observation 2378b8aa-2bc2-4fe6-9c79-389c9d63ac0c · outbound

This paper cites FastInst: A simple query-based model for real-time instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds FastInst: A simple query-based model for real-time instance segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.038969Z

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Observation 59a66870-b4c3-43ab-9c28-b646b54fdb25 · outbound

This paper cites TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:22:26.735447Z

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Observation 6282fee7-fd49-45a2-8d7c-9562e8d2e9e4 · outbound

This paper cites Hyyppa, O.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Hyyppa, O

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.022376Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 25782dad-0315-45ac-852d-c5af1df0946e · outbound

This paper cites PointGroup: Dual-set point grouping for 3D instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds PointGroup: Dual-set point grouping for 3D instance segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:28.004788Z

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Observation 50f7c140-7008-4538-b6f9-136e6ce885c8 · outbound

This paper cites LWSNet: A point-based segmentation network for leaf-wood separa- tion of individual trees.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds LWSNet: A point-based segmentation network for leaf-wood separa- tion of individual trees

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.987604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4475935d-bb19-43d9-8d9c-62615d90c5c2 · outbound

This paper cites Automated segmentation of individual tree structures using deep learn- ing over LiDAR point cloud data.Forests, 14(6):1159, 2023.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Automated segmentation of individual tree structures using deep learn- ing over LiDAR point cloud data.Forests, 14(6):1159, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.971877Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f6a9d9b0-739d-4a95-9403-ed48e53aefbf · outbound

This paper cites Top-down beats bottom-up in 3D in- stance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Top-down beats bottom-up in 3D in- stance segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.954262Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2685f36-350c-4489-b73d-08663d998a88 · outbound

This paper cites OneFormer3D: One transformer for unified point cloud segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds OneFormer3D: One transformer for unified point cloud segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.938877Z

Source-reported events for the cited work

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Observation 9cf714c6-36c9-4dad-b029-5dfe6ebc6717 · outbound

This paper cites Sensor agnos- tic semantic segmentation of structurally diverse and com- plex forest point clouds using deep learning.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Sensor agnos- tic semantic segmentation of structurally diverse and com- plex forest point clouds using deep learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.923321Z

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Observation 67a76495-bd15-4d99-b35a-553939b2af98 · outbound

This paper cites The Hungarian method for the assignment problem.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds The Hungarian method for the assignment problem

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.907065Z

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Observation b2ce7c5e-af61-4edc-beb4-98234bd4cee8 · outbound

This paper cites MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:22:27.221263Z

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Observation 8fc3e329-0265-41d0-a45e-4dc2e96f6617 · outbound

This paper cites Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.891786Z

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Observation ca472298-2992-4752-8379-000acb5baf2a · outbound

This paper cites 3D-QueryIS: A Query-based Framework for 3D Instance Segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 3D-QueryIS: A Query-based Framework for 3D Instance Segmentation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:22:27.196632Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88a51bb1-4122-4932-8e59-d0c44149393a · outbound

This paper cites Learnable earth parser: Discovering 3D proto- types in aerial scans.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Learnable earth parser: Discovering 3D proto- types in aerial scans

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.875882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12d3a93c-14f8-41b0-844f-6864df2231ea · outbound

This paper cites Query refinement transformer for 3D in- stance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Query refinement transformer for 3D in- stance segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.859836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 808437cb-90ff-4fc2-8111-7b7f4ab20381 · outbound

This paper cites Conditional DETR for fast training convergence.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Conditional DETR for fast training convergence

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.844012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 55c19eed-8042-415f-a983-c2c14a3cbe8d · outbound

This paper cites JSIS3D: Joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds JSIS3D: Joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.828241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78499b82-4aef-4740-856f-428103a189df · outbound

This paper cites Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.812062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41efbc9c-884a-4bd1-88b4-4a55cad8f142 · outbound

This paper cites FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T19:22:26.831136Z

Source-reported events for the cited work

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Observation c74a4c1e-c7e1-4f4f-a51d-cc26f3d4bee1 · outbound

This paper cites Qi, Li Yi, Hao Su, and Leonidas J.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Qi, Li Yi, Hao Su, and Leonidas J

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.794996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 43e419c2-27ea-4e79-881f-e8293eb466a0 · outbound

This paper cites Mask3D: Mask trans- former for 3D semantic instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Mask3D: Mask trans- former for 3D semantic instance segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.777869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 147fb449-a812-4748-94ab-197675cb4142 · outbound

This paper cites Spherical mask: Coarse-to-fine 3D point cloud instance segmentation with spherical repre- sentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Spherical mask: Coarse-to-fine 3D point cloud instance segmentation with spherical repre- sentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.760551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6dec3817-ae52-4096-ba50-877ae81cc60c · outbound

This paper cites Instance segmentation of individual tree crowns with YOLOv5: A comparison of approaches using the ForIn- stance benchmark LiDAR dataset.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Instance segmentation of individual tree crowns with YOLOv5: A comparison of approaches using the ForIn- stance benchmark LiDAR dataset

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.742899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38a53e1a-6499-4840-8e18-0dc1206eac18 · outbound

This paper cites Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.724871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 978fddce-3827-4eec-8d40-a570a4003cd5 · outbound

This paper cites Superpoint transformer for 3D scene instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Superpoint transformer for 3D scene instance segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.706246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 336cd33d-ca66-41a7-93eb-2d5c3fdc6a97 · outbound

This paper cites Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas Guibas.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas Guibas

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.689086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f3fb21b5-379b-4a5a-b165-84db1c943a3d · outbound

This paper cites 3D Forest: An application for descriptions of three- dimensional forest structures using terrestrial LiDAR.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds 3D Forest: An application for descriptions of three- dimensional forest structures using terrestrial LiDAR

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.672547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48309f5a-ece1-4c2c-b53e-9e3644746fec · outbound

This paper cites Vicari, Mathias Disney, Phil Wilkes, Andrew Burt, Kim Calders, and William Woodgate.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Vicari, Mathias Disney, Phil Wilkes, Andrew Burt, Kim Calders, and William Woodgate

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.655958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46ae3143-3805-4ee7-88b8-5684e82fdb48 · outbound

This paper cites Luu, Thanh Nguyen, and Chang D.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Luu, Thanh Nguyen, and Chang D

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.638958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82920318-8f6f-448b-8404-cbe3fdfedd03 · outbound

This paper cites A novel and effi- cient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds A novel and effi- cient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.619726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 493d5b8f-7b50-46ae-90cb-3eb9890c8033 · outbound

This paper cites LeWoS: A universal leaf-wood classification method to facil- itate the 3D modelling of large tropical trees using terrestrial LiDAR.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds LeWoS: A universal leaf-wood classification method to facil- itate the 3D modelling of large tropical trees using terrestrial LiDAR

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.602419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 53522e8f-a460-44a6-96f2-6a98eac3764c · outbound

This paper cites Tree segmentation and parameter measurement from point clouds using deep and handcrafted features.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Tree segmentation and parameter measurement from point clouds using deep and handcrafted features

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.584765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84c805b9-85ec-4364-9c1c-ad0ef543f3ce · outbound

This paper cites SGPN: Similarity group proposal network for 3D point cloud instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds SGPN: Similarity group proposal network for 3D point cloud instance segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.567835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b1259266-ff1e-415c-b7a5-922c26d07586 · outbound

This paper cites Associatively segmenting instances and semantics in point clouds.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Associatively segmenting instances and semantics in point clouds

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.549284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cad27f2a-c85f-4914-9bf4-c18b15721845 · outbound

This paper cites Point2Tree(P2T)—framework for parameter tun- ing of semantic and instance segmentation used with mobile laser scanning data in coniferous forest.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Point2Tree(P2T)—framework for parameter tun- ing of semantic and instance segmentation used with mobile laser scanning data in coniferous forest

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.524720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1898e9ee-0e25-4d99-bf0f-65e5d056513c · outbound

This paper cites SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmen- tation using laser scanning data.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmen- tation using laser scanning data

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.505188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5dc05991-a09e-4f9d-b5ed-c56447e4d920 · outbound

This paper cites TLS2trees: A scalable tree segmentation pipeline for TLS data.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds TLS2trees: A scalable tree segmentation pipeline for TLS data

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.485753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0f1a6f3-2f31-4da4-9e72-2660cbccea8d · outbound

This paper cites Detection, segmentation, and model fitting of individual tree stems from airborne laser scanning of forests using deep learning.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Detection, segmentation, and model fitting of individual tree stems from airborne laser scanning of forests using deep learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.468031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a4dc7b0-8f9a-4740-bfc7-55beb330f201 · outbound

This paper cites Filter- ing stems and branches from terrestrial laser scanning point clouds using deep 3-D fully convolutional networks.Remote Sensing, 10(8):1215, 2018.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Filter- ing stems and branches from terrestrial laser scanning point clouds using deep 3-D fully convolutional networks.Remote Sensing, 10(8):1215, 2018

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.450413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 013c84da-d731-43d2-b9b8-9d1cba5b1fc3 · outbound

This paper cites Towards accurate instance segmentation in large- scale LiDAR point clouds.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Towards accurate instance segmentation in large- scale LiDAR point clouds

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.433106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7e970b47-d771-4ecd-b0c7-2a978ba90851 · outbound

This paper cites Automated forest inventory: Analysis of high- density airborne LiDAR point clouds with 3D deep learning.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Automated forest inventory: Analysis of high- density airborne LiDAR point clouds with 3D deep learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.416359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5be667d2-73da-4616-b36d-335891416489 · outbound

This paper cites Learning ob- ject bounding boxes for 3D instance segmentation on point clouds.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Learning ob- ject bounding boxes for 3D instance segmentation on point clouds

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.398278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation daa402f3-c100-4a5d-94a8-24a9bb0244b4 · outbound

This paper cites SGI- Former: Semantic-guided and geometric-enhanced inter- leaving transformer for 3D instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds SGI- Former: Semantic-guided and geometric-enhanced inter- leaving transformer for 3D instance segmentation

Reference 65

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:22:27.146010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bbbdcfe8-fdc9-4a92-a14d-2b59bd4f9252 · outbound

This paper cites Yi, Wang Zhao, He Wang, Minhyuk Sung, and L.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Yi, Wang Zhao, He Wang, Minhyuk Sung, and L

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.379920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cca7b25e-d3dd-44ad-9aed-1984d76078c0 · outbound

This paper cites Torr, and Victor Prisacariu.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds Torr, and Victor Prisacariu

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.357777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 720dbc89-7c03-4c96-a942-f4e557f04e6f · outbound

This paper cites To- wards intricate stand structure: A novel individual tree seg- mentation method for ALS point cloud based on extreme off- set deep learning.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds To- wards intricate stand structure: A novel individual tree seg- mentation method for ALS point cloud based on extreme off- set deep learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.340753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b40e6173-4f2a-44ea-bf40-6c0a2594e813 · outbound

This paper cites MaskGroup: Hierarchical point grouping and masking for 3D instance segmentation.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds MaskGroup: Hierarchical point grouping and masking for 3D instance segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.316290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84163bf9-42cf-413a-8b90-ba419038c259 · outbound

This paper cites TreeStructor: Forest reconstruction with neural ranking.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds TreeStructor: Forest reconstruction with neural ranking

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.297756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5562a1a1-e8e6-409b-afc8-00dd2298b7c1 · outbound

This paper cites DETRs with collaborative hybrid assignments training.

ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds DETRs with collaborative hybrid assignments training

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:22:27.276725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Outbound references

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