Efficient 3D Reconstruction of Multiple Plants from UAV Images with Deep Learning | Proceedings of the 2024 16th International Conference on Machine Learning and Computing (2024)

1. Henrik Aanæs, RasmusRamsbøl Jensen, George Vogiatzis, Engin Tola, and AndersBjorholm Dahl. 2016. Large-scale data for multiple-view stereopsis. International Journal of Computer Vision 120 (2016), 153–168.Google ScholarDigital Library
2. Connelly Barnes, Eli Shechtman, Adam Finkelstein, and DanB Goldman. 2009. PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28, 3 (2009), 24.Google ScholarDigital Library
3. EF Berra and MV Peppa. 2020. Advances and challenges of UAV SFM MVS photogrammetry and remote sensing: Short review. In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). IEEE, 533–538.Google ScholarCross Ref
4. Zhen Chen, Hui Lv, Lu Lou, and JohnH Doonan. 2022. Fast and accurate 3D reconstruction of plants using mvsnet and multi-view images. In Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK 20. Springer, 390–399.Google Scholar
5. Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. 2020. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2495–2504.Google ScholarCross Ref
6. Xian-Feng Han, Hamid Laga, and Mohammed Bennamoun. 2019. Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era. IEEE transactions on pattern analysis and machine intelligence 43, 5 (2019), 1578–1604.Google Scholar
7. Jakob Iglhaut, Carlos Cabo, Stefano Puliti, Livia Piermattei, James O’Connor, and Jacqueline Rosette. 2019. Structure from motion photogrammetry in forestry: A review. Current Forestry Reports 5 (2019), 155–168.Google ScholarCross Ref
8. San Jiang, Wanshou Jiang, and Lizhe Wang. 2021. Unmanned aerial vehicle-based photogrammetric 3d mapping: A survey of techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine 10, 2 (2021), 135–171.Google ScholarCross Ref
9. Chenhui Lin, Hong Wang, Chengliang Liu, and Liang Gong. 2020. 3D reconstruction based plant-monitoring and plant-phenotyping platform. In 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). IEEE, 522–526.Google ScholarCross Ref
10. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2117–2125.Google ScholarCross Ref
11. Fusang Liu, Pengcheng Hu, Bangyou Zheng, Tao Duan, Binglin Zhu, and Yan Guo. 2021. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agricultural and Forest Meteorology 296 (2021), 108231.Google ScholarCross Ref
12. Elias Marks, Federico Magistri, and Cyrill Stachniss. 2022. Precise 3D reconstruction of plants from UAV imagery combining bundle adjustment and template matching. In 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2259–2265.Google ScholarDigital Library
13. Paul Merrell, Amir Akbarzadeh, Liang Wang, Philippos Mordohai, Jan-Michael Frahm, Ruigang Yang, David Nistér, and Marc Pollefeys. 2007. Real-time visibility-based fusion of depth maps. In 2007 IEEE 11th International Conference on Computer Vision. Ieee, 1–8.Google ScholarCross Ref
14. Seishi Ninomiya. 2022. High-throughput field crop phenotyping: current status and challenges. Breeding Science 72, 1 (2022), 3–18.Google ScholarCross Ref
15. Niall O’Mahony, Sean Campbell, Lenka Krpalkova, Daniel Riordan, Joseph Walsh, Aidan Murphy, and Conor Ryan. 2019. Computer Vision for 3D Perception: A Review. In Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 2. Springer, 788–804.Google Scholar
16. Yeping Peng, Mingbin Yang, Genping Zhao, and Guangzhong Cao. 2021. Binocular-vision-based structure from motion for 3-D reconstruction of plants. IEEE Geoscience and Remote Sensing Letters 19 (2021), 1–5.Google Scholar
17. Jessada Phattaralerphong and Hervé Sinoquet. 2005. A method for 3D reconstruction of tree crown volume from photographs: assessment with 3D-digitized plants. Tree Physiology 25, 10 (2005), 1229–1242.Google ScholarCross Ref
18. Worasit Sangjan, RebeccaJ McGee, and Sindhuja Sankaran. 2022. Optimization of UAV-based imaging and image processing orthom*osaic and point cloud approaches for estimating biomass in a forage crop. Remote Sensing 14, 10 (2022), 2396.Google ScholarCross Ref
19. JohannesL Schonberger and Jan-Michael Frahm. 2016. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4104–4113.Google ScholarCross Ref
20. JohannesL Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. 2016. Pixelwise view selection for unstructured multi-view stereo. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14. Springer, 501–518.Google ScholarCross Ref
21. Sergio Vélez, Rubén Vacas, Hugo Martín, David Ruano-Rosa, and Sara Álvarez. 2022. High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard (Pistacia vera L.) in Spain. Data 7, 11 (2022), 157.Google ScholarCross Ref
22. Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, and Marc Pollefeys. 2021. Patchmatchnet: Learned multi-view patchmatch stereo. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14194–14203.Google ScholarCross Ref
23. Xiang Wang, Chen Wang, Bing Liu, Xiaoqing Zhou, Liang Zhang, Jin Zheng, and Xiao Bai. 2021. Multi-view stereo in the deep learning era: A comprehensive review. Displays 70 (2021), 102102.Google ScholarCross Ref
24. Huang Yao, Rongjun Qin, and Xiaoyu Chen. 2019. Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing 11, 12 (2019), 1443.Google ScholarCross Ref
25. Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. 2018. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV). 767–783.Google ScholarDigital Library
26. Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan. 2019. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5525–5534.Google ScholarCross Ref
27. Yanchao Zhang, Hanxuan Wu, and Wen Yang. 2019. Forests growth monitoring based on tree canopy 3D reconstruction using UAV aerial photogrammetry. Forests 10, 12 (2019), 1052.Google ScholarCross Ref
28. Genping Zhao, Weitao Cai, Zhuowei Wang, Heng Wu, Yeping Peng, and Lianglun Cheng. 2022. Phenotypic parameters estimation of plants using deep learning-based 3-D reconstruction from single RGB image. IEEE Geoscience and Remote Sensing Letters 19 (2022), 1–5.Google Scholar

Index Terms

1. Efficient 3D Reconstruction of Multiple Plants from UAV Images with Deep Learning

   1. Computing methodologies

      1. Artificial intelligence

         1. Computer vision

• Time-of-Flight Camera Calibration for Improved 3D Reconstruction of Indoor Scenes

  ISCID '14: Proceedings of the 2014 Seventh International Symposium on Computational Intelligence and Design - Volume 02

  Calibration for Time-of-Flight (ToF) cameras are different from traditional color cameras, in that their calibration involves additional error compensation steps for the depth camera caused by complicated factors. Without this process, there may be ...

  Read More

• Dense 3D reconstruction combining depth and RGB information

  Dense 3D reconstruction has important applications in many fields. The existing depth information based methods are typically constrained in their effective camera-object distance which should be from 0.4m to 4m. We present a novel method that can ...

  Read More

• Fixed-Lens camera setup and calibrated image registration for multifocus multiview 3D reconstruction

  Abstract

  Image-based 3D reconstruction or 3D photogrammetry of small-scale objects including insects and biological specimens is challenging due to the use of a high magnification lens with inherently limited depth of field, and the object’s fine ...

  Read More