DCA-Net: Dilated Context Attention Network for MLS Point Cloud Semantic Segmentation

Fadet: a fusion-aware 3D detection network with cascaded feature enhancement for small object detection in autonomous driving

LERPNet: a lightweight and efficient range view point cloud semantic segmentation network

This paper replaces the Stem layer in FRNet with the proposed FD-Stem, which improves feature representation while reducing computational complexity, and introduces long-range modeling capability with limited additional parameters, enabling effective learning of both spatial and channel-wise representations.

PDES-Net: LiDAR point cloud semantic segmentation network based on point-wise distance encoding and pointed-seg head

Light Detection and Ranging (LiDAR)-based semantic segmentation is significant in advanced autonomous driving systems. However, it is challenging to achieve accurate and efficient semantic segmentation because of the sparse and uneven distribution of LiDAR point cloud data. To address the above problem, we propose a LiDAR point cloud semantic segmentation network based on point-wise distance encoding and pointed-seg head, named as PDES-Net. The point-wise distance encoding mechanism introduces normalized continuous depth information as a supplementary feature into the network, enhances the perception of both near and distant points, and reduce geometric information loss. The pointed-seg head module adaptively integrates multilevel features through learnable weight coefficients, and enhances the expressive ability of point-wise prediction. The performance of the proposed PDES-Net is evaluated on the publicly available benchmarks, SemanticKITTI and nuScenes, achieving mIoU of 68.9% and 78.9%, respectively. The proposed PDES-Net enhances segmentation performance while maintaining advantages in model parameters and inference speed. Overall, the network achieves a well-balanced trade-off between accuracy and computational efficiency.

Spmixnet: spatial-channel collaborative modeling for enhanced small-object segmentation in LiDAR point clouds

GALA-Net: geometry-enhanced adaptive local feature aggregation network for point cloud semantic segmentation

ABSTRACT Currently, existing point cloud semantic segmentation methods do not fully exploit surface geometric features. In particular, the depiction of object boundaries and the transition areas of curved surfaces is rather rough. On the other hand, the neighbourhood aggregation mostly follows a single strategy, making it difficult to simultaneously take into account the context and fine-grained differences and ignoring local details. To address these issues, this paper proposes a geometry-enhanced adaptive local feature aggregation network (GALA-Net). First, a geometric information embedding (GIE) module is introduced, which extracts pseudo-normal vectors and pseudo-curvatures of local point cloud regions as geometric priors, and incorporates multi-frequency sine–cosine encoding to capture multi-scale spatial relationships, yielding enhanced local geometric representations. Then, an adaptive feature fusion (AFF) module dynamically allocates fusion weights between semantic and geometric features, thereby alleviating channel coupling and neighbourhood noise amplification caused by simple concatenation. Next, a dual-path adaptive attention aggregation (DAAA) module jointly models semantic and positional attention and adaptively fuses them with max-pooled features to improve the robustness of local aggregation. In addition, a self-enhanced attention encoding (SEAE) module is designed to expand the feature representation space by extracting features through independent mapping branches and fusing them in a residual manner. The proposed model is evaluated on the S3DIS and ScanNetV2 datasets, achieving mIoU scores of 78.0% and 71.6%, respectively, which demonstrates its strong segmentation performance on indoor scenes.

Hierarchical attention fusion network with dynamic coordinate and group-aware enhancement for remote sensing semantic segmentation

Remote sensing image segmentation is challenged by large object scale variations, blurred boundaries, and complex backgrounds. To address these issues, we propose HAFNet, a hierarchical attention fusion network built upon U-Net with collaborative innovations in feature extraction, cross-layer fusion, and feature enhancement. First, we embed a Dynamic Gated Coordinate Attention (DGCA) module in the encoder. It incorporates coordinate information to capture direction-aware long-range dependencies and uses a dynamic gating mechanism to adaptively adjust attention weights along vertical and horizontal directions, improving geometric modeling of elongated roads and irregular water bodies. Second, a Multi-scale Dynamic Calibration Fusion Module (MDCFM) bridges the semantic gap between adjacent encoder layers. It enhances salient regions via spatial attention, generates bidirectional weight matrices for crosscalibration between low-level details and high-level semantics, and applies DGCA for direction-aware enhancement, mitigating semantic mismatch in feature fusion. Finally, a Group-aware Dynamic Focusing Module (GDFM) is introduced in the decoder. It partitions features along channels, applies CBAM independently to each group to capture multi-directional edge information, and employs dynamic weight recalibration to strengthen key regions while preserving feature integrity, enabling precise boundary reconstruction. Experiments on ISPRS Potsdam and LoveDA datasets show that HAFNet achieves superior mIoU and F1 scores over existing methods, particularly in small object segmentation and boundary refinement, demonstrating the effectiveness of the collaborative design of the three modules.