Year: 2025
Type: article
Abstract: Point cloud completion for cotton leaves is critical for accurately reconstructing complete shapes from sparse and significantly incomplete data. Traditional methods typically assume small missing ratios (≤25%), which limits their effectiveness for morphologically complex cotton leaves with severe sparsity (50–75%), large geometric distortions, and extensive point loss. To overcome these challenges, we introduce an end-to-end neural network that combines PF-Net and PointNet++ to effectively reconstruct dense, uniform point clouds from incomplete inputs. The model initially uses a multiresolution encoder to extract multiscale features from locally incomplete point clouds at different resolutions. By capturing both low-level and high-level attributes, these features significantly enhance the network’s ability to represent semantic content and geometric structure. Next, a point pyramid decoder generates missing point clouds hierarchically from layers at different depths, effectively reconstructing the fine details of the original structure. PointNet++ is then used to fuse and reshape the incomplete input point clouds with the generated missing points, yielding a fully reconstructed and uniformly distributed point cloud. To ensure effective task completion at different training stages, a loss function freezing strategy is employed, optimizing the network’s performance throughout the training process. Experimental evaluation on the cotton leaf dataset demonstrated that the proposed model outperformed PF-Net, reducing the Chamfer distance by 80.15% and the Earth Mover distance by 54.35%. These improvements underscore the model’s robustness in reconstructing sparse point clouds for precise agricultural phenotyping.
Source: Agriculture
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4414390899