An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation
The recent surge in diverse 3D datasets spanning various scales and applications marks a significant advancement in the field. However, the comprehensive process of data acquisition, refinement, and annotation at a large scale poses a formidable challenge, particularly for individual researchers and...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4240 |
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| author | Meida Chen Kangle Han Zifan Yu Andrew Feng Yu Hou Suya You Lucio Soibelman |
| author_facet | Meida Chen Kangle Han Zifan Yu Andrew Feng Yu Hou Suya You Lucio Soibelman |
| author_sort | Meida Chen |
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| description | The recent surge in diverse 3D datasets spanning various scales and applications marks a significant advancement in the field. However, the comprehensive process of data acquisition, refinement, and annotation at a large scale poses a formidable challenge, particularly for individual researchers and small teams. To this end, we present a novel synthetic 3D point cloud generation framework that can produce detailed outdoor aerial photogrammetric 3D datasets with accurate ground truth annotations without the labor-intensive and time-consuming data collection/annotation processes. Our pipeline procedurally generates synthetic environments, mirroring real-world data collection and 3D reconstruction processes. A key feature of our framework is its ability to replicate consistent quality, noise patterns, and diversity similar to real-world datasets. This is achieved by adopting UAV flight patterns that resemble those used in real-world data collection processes (e.g., the cross-hatch flight pattern) across various synthetic terrains that are procedurally generated, thereby ensuring data consistency akin to real-world scenarios. Moreover, the generated datasets are enriched with precise semantic and instance annotations, eliminating the need for manual labeling. Our approach has led to the development and release of the Semantic Terrain Points Labeling—Synthetic 3D (STPLS3D) benchmark, an extensive outdoor 3D dataset encompassing over 16 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>km</mi><mn>2</mn></msup></semantics></math></inline-formula>, featuring up to 19 semantic labels. We also collected, reconstructed, and annotated four real-world datasets for validation purposes. Extensive experiments on these datasets demonstrate our synthetic datasets’ effectiveness, superior quality, and their value as a benchmark dataset for further point cloud research. |
| format | Article |
| id | doaj-art-6d431cb8572e4f4e824f0d7c5f0fb111 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6d431cb8572e4f4e824f0d7c5f0fb1112024-11-26T18:20:08ZengMDPI AGRemote Sensing2072-42922024-11-011622424010.3390/rs16224240An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style TranslationMeida Chen0Kangle Han1Zifan Yu2Andrew Feng3Yu Hou4Suya You5Lucio Soibelman6Institute for Creative Technologies, University of Southern California, Los Angeles, CA 90094, USAAstani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USADepartment of Computer Science, Arizona State University, Tempe, AZ 85281, USAInstitute for Creative Technologies, University of Southern California, Los Angeles, CA 90094, USAThe Department of Construction Management, Western New England University, Springfield, MA 01119, USADEVCOM Army Research Laboratory, Los Angeles, CA 90089, USAAstani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USAThe recent surge in diverse 3D datasets spanning various scales and applications marks a significant advancement in the field. However, the comprehensive process of data acquisition, refinement, and annotation at a large scale poses a formidable challenge, particularly for individual researchers and small teams. To this end, we present a novel synthetic 3D point cloud generation framework that can produce detailed outdoor aerial photogrammetric 3D datasets with accurate ground truth annotations without the labor-intensive and time-consuming data collection/annotation processes. Our pipeline procedurally generates synthetic environments, mirroring real-world data collection and 3D reconstruction processes. A key feature of our framework is its ability to replicate consistent quality, noise patterns, and diversity similar to real-world datasets. This is achieved by adopting UAV flight patterns that resemble those used in real-world data collection processes (e.g., the cross-hatch flight pattern) across various synthetic terrains that are procedurally generated, thereby ensuring data consistency akin to real-world scenarios. Moreover, the generated datasets are enriched with precise semantic and instance annotations, eliminating the need for manual labeling. Our approach has led to the development and release of the Semantic Terrain Points Labeling—Synthetic 3D (STPLS3D) benchmark, an extensive outdoor 3D dataset encompassing over 16 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>km</mi><mn>2</mn></msup></semantics></math></inline-formula>, featuring up to 19 semantic labels. We also collected, reconstructed, and annotated four real-world datasets for validation purposes. Extensive experiments on these datasets demonstrate our synthetic datasets’ effectiveness, superior quality, and their value as a benchmark dataset for further point cloud research.https://www.mdpi.com/2072-4292/16/22/4240synthetic point cloud datasetaerial photogrammetrysemantic and instance segmentation |
| spellingShingle | Meida Chen Kangle Han Zifan Yu Andrew Feng Yu Hou Suya You Lucio Soibelman An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation Remote Sensing synthetic point cloud dataset aerial photogrammetry semantic and instance segmentation |
| title | An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation |
| title_full | An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation |
| title_fullStr | An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation |
| title_full_unstemmed | An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation |
| title_short | An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation |
| title_sort | aerial photogrammetry benchmark dataset for point cloud segmentation and style translation |
| topic | synthetic point cloud dataset aerial photogrammetry semantic and instance segmentation |
| url | https://www.mdpi.com/2072-4292/16/22/4240 |
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