With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows.

Image-based forest 3D reconstruction from multi-view imagery can generate stand-level point clouds and provide data support for extracting individual-tree structural parameters, such as diameter at breast height (DBH) and tree height. Three-dimensional reconstruction of forest ecosystems is fundamental to forest resource monitoring and biomass assessment. However, forest scenes are characterized by frequent branch and leaf occlusions, repetitive bark and foliage textures, and complex illumination conditions. These factors often introduce ambiguities into feature matching and cost aggregation in convolutional neural network (CNN)-based multi-view stereo (MVS) methods, resulting in incomplete point clouds and depth estimation errors. To address these challenges, this study proposes CasFormer, a cascaded multi-view 3D reconstruction model designed for near-ground handheld forest imagery. Built upon a cascaded cost-volume framework, CasFormer constructs a Self-and-Cross Attention Feature Pyramid Network (SC-FPN) and incorporates a Positional Encoding Generator (PEG) to inject camera geometric priors into feature representations, thereby enhancing feature discriminability in repetitive weak-texture regions. In addition, a pixel-wise visibility-weighted cost volume is used to adaptively adjust source-view aggregation weights according to matching confidence, reducing the influence of occluded or low-quality source views on the cost volume and improving the stability of depth estimation in forest scenes. Experiments across three forest plots show that CasFormer achieves the highest overall single-tree matching rate (99.14%) and outperforms the competing methods in tree-height accuracy and point-cloud density at all three plots. Its mean DAE is 92.87%, exceeding FS-MVSNet and MVSFormer by 0.97 and 2.78 percentage points, respectively. CasFormer attains the lowest DBH RMSE at Plots 1 and 2; at Plot 3, its RMSE is comparable to those of MVSFormer and FS-MVSNet. Its point-cloud densities are 3.87, 2.17, and 4.87 × 10⁶ points/m² at Plots 1, 2, and 3, respectively, the highest among the methods evaluated at each plot. These results demonstrate that CasFormer improves plot-scale forest 3D reconstruction using handheld RGB imagery that can be acquired with minimal effort. It provides an effective image-based reconstruction method for extracting individual-tree parameters and supports sustainable smart forestry monitoring.