The Process of Non-Rigid Transformation of Part of the Point Cloud After Clustering, Where Different Color Indicates Different Cluster Category
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Summary
<img src="https://www.newswise.com/legacy/image.php?image=https://media.newswise.com/pio/articles/857874/6ae9c6a0b3917_1_857874.jpg&width=100&height=150" alt="Newswise image" />Researchers have developed a multi-scale dual-discriminator generative adversarial network (MSDDG) to complete missing point clouds of plants, a common problem when optical sensors capture complex canopies. The method combines a multi-resolution encoder (MRE), a multi-scale point cloud generator (MSPG), and two discriminators--one judging three-dimensional (3D) spatial distance and one judging multi-view projected silhouettes. Trained on Plant4L, a new dataset covering sunflower, pumpkin, luffa and eggplant, the framework reconstructs occluded leaves and stems with high geometric fidelity. It also supports single-view 3D reconstruction, improving leaf-area and inclination-angle estimates. The work addresses a key bottleneck in plant phenotyping: incomplete 3D data caused by viewpoint occlusion and sensor limitations.
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