The accurate leaf-wood separation of individual trees from point clouds is an important yet challenging task. Many existing methods rely on manual features that are time-consuming and labor-intensive to distinguish between leaf and wood points. However, due to the complex interlocking structure of leaves and wood in the canopy, these methods have not yielded satisfactory results. Therefore, this paper proposes an end-to-end LWSNet to separate leaf and wood points within the canopy. First, we consider the linear and scattering distribution characteristics of leaf and wood points and calculate local geometric features with distinguishing properties to enrich the original point cloud information. Then, we fuse the local contextual information for feature enhancement and select more representative features through a rearrangement attention mechanism. Finally, we use a residual connection during the decoding stage to improve the robustness of the model and achieve efficient leaf-wood separation. The proposed LWSNet is tested on eight species of trees with different characteristics and sizes. The average F1 score for leaf-wood separation is as high as 97.29%. The results show that this method outperforms the state-of-the-art leaf-wood separation methods in previous studies, and can accurately and robustly separate leaves and wood in trees of different species, sizes, and structures. This study extends the leaf-wood separation of tree point clouds in an end-to-end manner and demonstrates that the deep-learning segmentation algorithm has a great potential for processing tree and plant point clouds with complex morphological traits.

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non‐overlapping special/rare categories, for example, rail track, track bed etc. To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation, we introduce RailPC, a new point cloud benchmark. RailPC provides a large‐scale dataset with rich annotations for semantic segmentation in the railway environment. Notably, RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning (MLS) point cloud dataset and is the first railway‐specific 3D dataset for semantic segmentation. It covers a total of nearly 25 km railway in two different scenes (urban and mountain), with 3 billion points that are finely labelled as 16 most typical classes with respect to railway, and the data acquisition process is completed in China by MLS systems. Through extensive experimentation, we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results. Based on our findings, we establish some critical challenges towards railway‐scale point cloud semantic segmentation. The dataset is available at https://github.com/NNU‐GISA/GISA‐RailPC , and we will continuously update it based on community feedback.

Pre-trained vision-language models (VLMs) like CLIP [1], equipped with parameter-efficient tuning (PET) methods like prompting [2], have shown impressive knowledge transferability on new downstream tasks, but they are still prone to be limited by catastrophic forgetting and overfitting dilemma due to large gaps among tasks. Furthermore, the underlying physical mechanisms of prompt-based tuning methods (especially for visual prompting) remain largely unexplored. It is unclear why these methods work solely based on learnable parameters as prompts for adaptation. To address the above challenges, we present a new prompt-based framework for vision-language models, termed Uni-prompt. Our framework transfers VLMs to downstream tasks by designing visual prompts from an attention perspective that reduces the transfer/solution space, which enables the vision model to focus on task-relevant regions of the input image while also learning task-specific knowledge. Additionally, Uni-prompt further aligns visual-text prompts learning through a pretext task with masked representation modeling interactions, which implicitly learns a global cross-modal matching between visual and language concepts for consistency. We conduct extensive experiments on the few-shot classification task and achieve significant improvement using our Uni-prompt method while requiring minimal extra parameters cost.

Background: Glaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough.Methods: This paper proposes a new encoder–decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder–decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup.Results: The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively.Conclusions: This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.