SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation
The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of whi...
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MDPI AG
2025-04-01
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| author | Abdollah Zakeri Bikram Koirala Jiming Kang Venkatesh Balan Weihang Zhu Driss Benhaddou Fatima A. Merchant |
| author_facet | Abdollah Zakeri Bikram Koirala Jiming Kang Venkatesh Balan Weihang Zhu Driss Benhaddou Fatima A. Merchant |
| author_sort | Abdollah Zakeri |
| collection | DOAJ |
| description | The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white <i>Agaricus bisporus</i> and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1024 points and the 6D Gram–Schmidt rotation representation yields optimal results, achieving an average rotational error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.67</mn><mo>°</mo></mrow></semantics></math></inline-formula> on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model, further, generalizes well to real-world data, attaining a mean angle difference of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.68</mn><mo>°</mo></mrow></semantics></math></inline-formula> on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry. |
| format | Article |
| id | doaj-art-3d032906aad047b4b71b3bb0ffb4f02d |
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| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-3d032906aad047b4b71b3bb0ffb4f02d2025-08-20T02:28:20ZengMDPI AGComputers2073-431X2025-04-0114412810.3390/computers14040128SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose EstimationAbdollah Zakeri0Bikram Koirala1Jiming Kang2Venkatesh Balan3Weihang Zhu4Driss Benhaddou5Fatima A. Merchant6Department of Computer Science, University of Houston, Houston, TX 77004, USADepartment of Engineering Technology, University of Houston, Houston, TX 77004, USADepartment of Engineering Technology, University of Houston, Houston, TX 77004, USADepartment of Engineering Technology, University of Houston, Houston, TX 77004, USADepartment of Engineering Technology, University of Houston, Houston, TX 77004, USADepartment of Engineering Technology, University of Houston, Houston, TX 77004, USADepartment of Computer Science, University of Houston, Houston, TX 77004, USAThe mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white <i>Agaricus bisporus</i> and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1024 points and the 6D Gram–Schmidt rotation representation yields optimal results, achieving an average rotational error of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.67</mn><mo>°</mo></mrow></semantics></math></inline-formula> on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model, further, generalizes well to real-world data, attaining a mean angle difference of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.68</mn><mo>°</mo></mrow></semantics></math></inline-formula> on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry.https://www.mdpi.com/2073-431X/14/4/128synthetic datasetmushroom detectionmushroom pose estimationpoint clouddeep learningcomputer vision |
| spellingShingle | Abdollah Zakeri Bikram Koirala Jiming Kang Venkatesh Balan Weihang Zhu Driss Benhaddou Fatima A. Merchant SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation Computers synthetic dataset mushroom detection mushroom pose estimation point cloud deep learning computer vision |
| title | SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation |
| title_full | SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation |
| title_fullStr | SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation |
| title_full_unstemmed | SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation |
| title_short | SMS3D: 3D Synthetic Mushroom Scenes Dataset for 3D Object Detection and Pose Estimation |
| title_sort | sms3d 3d synthetic mushroom scenes dataset for 3d object detection and pose estimation |
| topic | synthetic dataset mushroom detection mushroom pose estimation point cloud deep learning computer vision |
| url | https://www.mdpi.com/2073-431X/14/4/128 |
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