Scaling Up Forest Vision with Synthetic Data | Paper Citation Record

Observation 4e6fa8b6-95e3-45a3-b14e-b9ad70d61f70 · outbound

This paper cites The future of tropical forests under the united nations sustainable development goals.

Scaling Up Forest Vision with Synthetic Data The future of tropical forests under the united nations sustainable development goals

Reference 1

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Observation 5b26f4f2-74a5-4ba1-b801-bea0000e2627 · outbound

This paper cites The shape of trees: Reimagining forest ecology in three dimensions with remote sensing.

Scaling Up Forest Vision with Synthetic Data The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

Reference 2

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Observation b4d34eb8-cc4d-4c2d-8ab4-06721f9e252d · outbound

This paper cites Close-range remote sensing of forests: The state of the art, challenges, and opportunities for systems and data acquisitions.

Scaling Up Forest Vision with Synthetic Data Close-range remote sensing of forests: The state of the art, challenges, and opportunities for systems and data acquisitions

Reference 3

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Observation bfd9ea6a-9b23-4f44-9de9-dc0303bc4e84 · outbound

This paper cites The global ecosystem dynamics investigation: High-resolution laser ranging of the earth’s forests and topography.

Scaling Up Forest Vision with Synthetic Data The global ecosystem dynamics investigation: High-resolution laser ranging of the earth’s forests and topography

Reference 4

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Observation 5d5e864d-424d-415d-a512-ab1dfec1161d · outbound

This paper cites Global canopy height regression and uncertainty estimation from gedi lidar waveforms with deep ensembles.

Scaling Up Forest Vision with Synthetic Data Global canopy height regression and uncertainty estimation from gedi lidar waveforms with deep ensembles

Reference 5

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Observation 63dd13d9-154f-4f81-bacd-998cc2aac57c · outbound

This paper cites Area-based vs tree-centric approaches to mapping forest carbon in southeast asian forests from airborne laser scanning data.

Scaling Up Forest Vision with Synthetic Data Area-based vs tree-centric approaches to mapping forest carbon in southeast asian forests from airborne laser scanning data

Reference 6

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Observation e17d2c3d-7af8-4398-8982-285ba6eb4864 · outbound

This paper cites Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset.

Scaling Up Forest Vision with Synthetic Data Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset

Reference 7

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Observation 4e17c7d3-89ee-4195-b0d3-3cb7da2bbcfa · outbound

This paper cites Monitoring ash dieback (hymenoscyphus fraxineus) in british forests using hyperspectral remote sensing.

Scaling Up Forest Vision with Synthetic Data Monitoring ash dieback (hymenoscyphus fraxineus) in british forests using hyperspectral remote sensing

Reference 8

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Observation ec6ccc53-0125-4859-a02a-43b95bbdf9b3 · outbound

This paper cites o r \"a l \.

Scaling Up Forest Vision with Synthetic Data o r \"a l \

Reference 9

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Observation 653c6f6a-a7ab-4619-91eb-7914d1fcc546 · outbound

This paper cites Towards a worldwide wood economics spectrum.

Scaling Up Forest Vision with Synthetic Data Towards a worldwide wood economics spectrum

Reference 10

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Observation b2a7acc1-4b35-4243-9684-042b2abb3dd6 · outbound

This paper cites Crown plasticity and competition for canopy space: a new spatially implicit model parameterized for 250 north american tree species.

Scaling Up Forest Vision with Synthetic Data Crown plasticity and competition for canopy space: a new spatially implicit model parameterized for 250 north american tree species

Reference 11

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Observation 148070e6-dece-4dba-9646-afa6c133aaa7 · outbound

This paper cites Global atmospheric methane uptake by upland tree woody surfaces.

Scaling Up Forest Vision with Synthetic Data Global atmospheric methane uptake by upland tree woody surfaces

Reference 12

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Observation 83d80b45-177b-4139-8f13-a97e1b520167 · outbound

This paper cites Instance segmentation of individual tree crowns with yolov5: A comparison of approaches using the forinstance benchmark lidar dataset.

Scaling Up Forest Vision with Synthetic Data Instance segmentation of individual tree crowns with yolov5: A comparison of approaches using the forinstance benchmark lidar dataset

Reference 13

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Observation aa48f2a1-9eb6-41d1-a3a0-7294a29728d1 · outbound

This paper cites Accurate tropical forest individual tree crown delineation from aerial rgb imagery using mask r-cnn.

Scaling Up Forest Vision with Synthetic Data Accurate tropical forest individual tree crown delineation from aerial rgb imagery using mask r-cnn

Reference 14

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Observation cfadf3e2-653d-4980-86a7-a8a6c00b107c · outbound

This paper cites Point2tree (p2t)—framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest.

Scaling Up Forest Vision with Synthetic Data Point2tree (p2t)—framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest

Reference 15

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Observation e94bb428-574d-4ca9-8c49-7fe7238efd7f · outbound

This paper cites Automated forest inventory: analysis of high-density airborne lidar point clouds with 3d deep learning.

Scaling Up Forest Vision with Synthetic Data Automated forest inventory: analysis of high-density airborne lidar point clouds with 3d deep learning

Reference 16

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Observation e7209632-1bc0-43a3-9ea8-3393a1a7558a · outbound

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

Scaling Up Forest Vision with Synthetic Data SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

Reference 17

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Observation 83e20919-5363-493c-aec7-9a9898f183b0 · outbound

This paper cites The bitter lesson.

Scaling Up Forest Vision with Synthetic Data The bitter lesson

Reference 18

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Observation 003db79a-8ebb-4af5-8ee1-ea226689583e · outbound

This paper cites a fer, Lukas Winiwarter, Nina Kra s ovec, Fabian E Fassnacht, and Bernhard H \.

Scaling Up Forest Vision with Synthetic Data a fer, Lukas Winiwarter, Nina Kra s ovec, Fabian E Fassnacht, and Bernhard H \

Reference 19

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Observation 69b4d991-50c3-4a0b-8794-c7be6f020fbb · outbound

This paper cites Laser scanning reveals potential underestimation of biomass carbon in temperate forest.

Scaling Up Forest Vision with Synthetic Data Laser scanning reveals potential underestimation of biomass carbon in temperate forest

Reference 20

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Observation 41cc7c65-2632-4331-9824-639c6377ce52 · outbound

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

Scaling Up Forest Vision with Synthetic Data FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

Reference 21

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Observation 3ece7445-893b-4ab9-b77f-02185ea6d55d · outbound

This paper cites Dataset meta-level and statistical features affect machine learning performance.

Scaling Up Forest Vision with Synthetic Data Dataset meta-level and statistical features affect machine learning performance

Reference 22

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Observation 0b24d197-43ce-4313-a65f-3a7e075dacb0 · outbound

This paper cites Real-time human pose recognition in parts from single depth images.

Scaling Up Forest Vision with Synthetic Data Real-time human pose recognition in parts from single depth images

Reference 23

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Observation 9ba6f71b-44e7-472a-8f1f-8b670630cc95 · outbound

This paper cites HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles.

Scaling Up Forest Vision with Synthetic Data HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles

Reference 24

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Observation 14e5a76b-40c1-4c59-8885-5b6299951840 · outbound

This paper cites Virtual worlds as proxy for multi-object tracking analysis.

Scaling Up Forest Vision with Synthetic Data Virtual worlds as proxy for multi-object tracking analysis

Reference 25

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Observation b9fa12f3-3c61-4438-ac48-d046fa90e3a7 · outbound

This paper cites Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding.

Scaling Up Forest Vision with Synthetic Data Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding

Reference 26

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Observation 774f4c86-28b3-40e4-983b-043fd29db4c3 · outbound

This paper cites Synthetic datasets for autonomous driving: A survey.

Scaling Up Forest Vision with Synthetic Data Synthetic datasets for autonomous driving: A survey

Reference 27

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Observation 683572cc-573a-4af9-992b-b18269ff14da · outbound

This paper cites Unreal engine environment generation (pcg): a comparative overview of existing tools.

Scaling Up Forest Vision with Synthetic Data Unreal engine environment generation (pcg): a comparative overview of existing tools

Reference 28

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Observation 69bb31d8-8fbf-4905-b563-f28099df1050 · outbound

This paper cites Tree detection and diameter estimation based on deep learning.

Scaling Up Forest Vision with Synthetic Data Tree detection and diameter estimation based on deep learning

Reference 29

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Observation 43cbb650-c535-415f-b5a3-1164faa91af2 · outbound

This paper cites M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset.

Scaling Up Forest Vision with Synthetic Data M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset

Reference 30

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Observation 16a87781-f0b4-4945-bd59-c9f42cb469ce · outbound

This paper cites Spread: A large-scale, high-fidelity synthetic dataset for multiple forest vision tasks.

Scaling Up Forest Vision with Synthetic Data Spread: A large-scale, high-fidelity synthetic dataset for multiple forest vision tasks

Reference 31

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Observation f3e4a13f-0602-4e45-b88d-f11b4ffac87e · outbound

This paper cites Infinite nature: Perpetual view generation of natural scenes from a single image.

Scaling Up Forest Vision with Synthetic Data Infinite nature: Perpetual view generation of natural scenes from a single image

Reference 32

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Observation 25ac3c89-c60a-40c1-bf6a-d45e2ec3be3d · outbound

This paper cites Virtual laser scanning with helios++: A novel take on ray tracing-based simulation of topographic full-waveform 3d laser scanning.

Scaling Up Forest Vision with Synthetic Data Virtual laser scanning with helios++: A novel take on ray tracing-based simulation of topographic full-waveform 3d laser scanning

Reference 33

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Observation 557dd9aa-85ba-4798-a604-012b2f8f968e · outbound

This paper cites Unsupervised semantic and instance segmentation of forest point clouds.

Scaling Up Forest Vision with Synthetic Data Unsupervised semantic and instance segmentation of forest point clouds

Reference 34

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Observation 24c2a9c3-4476-4501-a2db-1ed752183e54 · outbound

This paper cites Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees.

Scaling Up Forest Vision with Synthetic Data Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees

Reference 35

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This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

Scaling Up Forest Vision with Synthetic Data Domain randomization for transferring deep neural networks from simulation to the real world

Reference 36

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Observation 7fc705d9-2cf7-40dd-aad8-904c167c46a2 · outbound

This paper cites Structured domain randomization: Bridging the reality gap by context-aware synthetic data.

Scaling Up Forest Vision with Synthetic Data Structured domain randomization: Bridging the reality gap by context-aware synthetic data

Reference 37

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Observation e92ccb03-3c44-4e23-89af-2e1b61f79aaf · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Scaling Up Forest Vision with Synthetic Data Model-agnostic meta-learning for fast adaptation of deep networks

Reference 38

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Observation 5e0710ec-873c-4e28-83a7-c4e53c34664b · outbound

This paper cites Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets.

Scaling Up Forest Vision with Synthetic Data Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets

Reference 39

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Observation 7d4067f7-df53-46b9-b693-fa745971b5eb · outbound

This paper cites A high-resolution canopy height model of the earth.

Scaling Up Forest Vision with Synthetic Data A high-resolution canopy height model of the earth

Reference 40

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Observation fa6e7600-57f9-4465-a121-f8800e8b5cb3 · outbound

This paper cites 3d modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping.

Scaling Up Forest Vision with Synthetic Data 3d modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Reference 41

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Observation 6be0f3d6-f744-4d5a-a428-3cde9770867c · outbound

This paper cites Enhancing uav--sfm 3d model accuracy in high-relief landscapes by incorporating oblique images.

Scaling Up Forest Vision with Synthetic Data Enhancing uav--sfm 3d model accuracy in high-relief landscapes by incorporating oblique images

Reference 42

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Observation d8f90230-f71a-4c84-b1ba-7709d8475588 · outbound

This paper cites Technological advances in close range sensors and methodological complexities for measuring forest structure and disturbance.

Scaling Up Forest Vision with Synthetic Data Technological advances in close range sensors and methodological complexities for measuring forest structure and disturbance

Reference 43

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Observation b94804e4-9ea1-48f2-bf07-a7bd67a7d8e4 · outbound

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

Scaling Up Forest Vision with Synthetic Data Towards accurate instance segmentation in large-scale LiDAR point clouds

Reference 44

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This paper cites a , Harri Kaartinen, Matti Lehtom \.

Scaling Up Forest Vision with Synthetic Data a , Harri Kaartinen, Matti Lehtom \

Reference 45

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Observation 8f79756b-24dc-4eff-a6fe-f5dde65d4232 · outbound

This paper cites Large-area virtual forests from terrestrial laser scanning data.

Scaling Up Forest Vision with Synthetic Data Large-area virtual forests from terrestrial laser scanning data

Reference 46

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This paper cites Carla: An open urban driving simulator.

Scaling Up Forest Vision with Synthetic Data Carla: An open urban driving simulator

Reference 47

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Observation 7ee131c0-b91c-4ab6-87e9-f153c3ccb9ff · outbound

This paper cites Airsim-w: A simulation environment for wildlife conservation with uavs.

Scaling Up Forest Vision with Synthetic Data Airsim-w: A simulation environment for wildlife conservation with uavs

Reference 48

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This paper cites Deep learning with simulated laser scanning data for 3d point cloud classification.

Scaling Up Forest Vision with Synthetic Data Deep learning with simulated laser scanning data for 3d point cloud classification

Reference 49

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This paper cites a fer, Hannah Weiser, Lukas Winiwarter, Bernhard H \.

Scaling Up Forest Vision with Synthetic Data a fer, Hannah Weiser, Lukas Winiwarter, Bernhard H \

Reference 50

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This paper cites Implications of 3d forest stand reconstruction methods for radiative transfer modeling: A case study in the temperate deciduous forest.

Scaling Up Forest Vision with Synthetic Data Implications of 3d forest stand reconstruction methods for radiative transfer modeling: A case study in the temperate deciduous forest

Reference 51

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This paper cites Essay on the architecture and dynamics of growth of tropical trees.

Scaling Up Forest Vision with Synthetic Data Essay on the architecture and dynamics of growth of tropical trees

Reference 52

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This paper cites Forest models defined by field measurements: estimation, error analysis and dynamics.

Scaling Up Forest Vision with Synthetic Data Forest models defined by field measurements: estimation, error analysis and dynamics

Reference 53

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This paper cites Semantic classification in uncolored 3d point clouds using multiscale features.

Scaling Up Forest Vision with Synthetic Data Semantic classification in uncolored 3d point clouds using multiscale features

Reference 54

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This paper cites Pointgroup: Dual-set point grouping for 3d instance segmentation.

Scaling Up Forest Vision with Synthetic Data Pointgroup: Dual-set point grouping for 3d instance segmentation

Reference 55

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

Scaling Up Forest Vision with Synthetic Data 4d spatio-temporal convnets: Minkowski convolutional neural networks

Reference 56

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Observation cfdfe82d-e76d-427c-8c8c-10f2919cdb81 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Scaling Up Forest Vision with Synthetic Data UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 57

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This paper cites 3d gaussian splatting for real-time radiance field rendering.

Scaling Up Forest Vision with Synthetic Data 3d gaussian splatting for real-time radiance field rendering

Reference 58

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This paper cites Towards multimodal open-set domain generalization and adaptation through self-supervision.

Scaling Up Forest Vision with Synthetic Data Towards multimodal open-set domain generalization and adaptation through self-supervision

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Observation 55b707c4-67a0-4100-92be-b4a834c80fbd · outbound

This paper cites Multimodal cross-domain few-shot learning for egocentric action recognition.

Scaling Up Forest Vision with Synthetic Data Multimodal cross-domain few-shot learning for egocentric action recognition

Reference 60

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Observation 2dbeee67-4b3e-45d7-9633-603a7134fac0 · outbound

This paper cites Point transformer v3: Simpler faster stronger.

Scaling Up Forest Vision with Synthetic Data Point transformer v3: Simpler faster stronger

Reference 61

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Observation 5777a489-fde0-4a32-860f-164c33ce030d · outbound

This paper cites ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds.

Scaling Up Forest Vision with Synthetic Data ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds

Reference 62

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Observation bb911a96-8780-4280-ab81-4976bf8cff90 · outbound

This paper cites Scaling vision transformers.

Scaling Up Forest Vision with Synthetic Data Scaling vision transformers

Reference 63

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This paper cites Weighing trees with lasers: advances, challenges and opportunities.

Scaling Up Forest Vision with Synthetic Data Weighing trees with lasers: advances, challenges and opportunities

Reference 64

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