Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a Geometric Contextual Feature Aggregation (GCFA) module and a Multi-scale Feature Adaptive Fusion (MFAF) module. The GCFA module consists of three primary blocks: (1) a Geometric Edge Representation block, designed to leverage spatial relative position and orientation information between the center point and its neighbors to capture detailed local geometric structural relations; (2) a Point Geometry Prior block, aimed at extracting explicit geometric priors for each point from raw point clouds. This block is lightweight and parameter-free; (3) a Geometry-Aware Attentive Pooling block, which combines semantic features with learned geometric representations, enabling the learning and aggregation of informative local contextual features. Our proposed MFAF module integrates multi-scale features by introducing an adaptive fusion approach. It effectively bridges the semantic gap between the encoder and decoder and mitigates the information loss caused by random sampling. Extensive experimental results on three large-scale benchmark datasets including S3DIS, Toronto3D, and SemanticKITTI demonstrate the superior performance of our proposed GAF-Net. 

GAF-Net: Geometric Contextual Feature Aggregation and Adaptive Fusion for Large-Scale Point Cloud Semantic Segmentation

LEARD-Net: Semantic segmentation for large-scale point cloud scene

Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, separated local regions from the general sampling architecture corrupt the structural information of the instances, and the inherent relationships between adjacent local regions lack exploration, while local structural information is crucial in a transformer-based 3D point cloud model. Therefore, in this paper, we propose a novel module named Local Context Propagation (LCP) to exploit the message passing between neighboring local regions and make their representations more informative and discriminative. More specifically, we use the overlap points of adjacent local regions (which statistically show to be prevalent) as intermediaries, then re-weight the features of these shared points from different local regions before passing them to the next layers. Inserting the LCP module between two transformer layers results in a significant improvement in network expressiveness. Finally, we design a flexible LCPFormer architecture equipped with the LCP module. The proposed method is applicable to different tasks and outperforms various transformer-based methods in benchmarks including 3D shape classification and dense prediction tasks such as 3D object detection and semantic segmentation. Code will be released for reproduction.

pdf/lcpformer-towards-effective-3d-point-cloud-analysis-via-1ipdusxp.pdf

LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers

At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on optical image 3D reconstruction methods in the underwater environment. However, due to the wide application of sonar in underwater 3D reconstruction, this paper also introduces and summarizes the underwater 3D reconstruction based on acoustic image and optical–acoustic image fusion methods. First, this paper uses the Citespace software to visually analyze the existing literature of underwater images and intuitively analyze the hotspots and key research directions in this field. Second, the particularity of underwater environments compared with conventional systems is introduced. Two scientific problems are emphasized by engineering problems encountered in optical image reconstruction: underwater image degradation and the calibration of underwater cameras. Then, in the main part of this paper, we focus on the underwater 3D reconstruction methods based on optical images, acoustic images and optical–acoustic image fusion, reviewing the literature and classifying the existing solutions. Finally, potential advancements in this field in the future are considered.

https://www.mdpi.com/2077-1312/11/5/949/pdf?version=1682696847

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

The point cloud feature aggregation, which learns discriminative features from the disordered points, plays a key role for large-scale point cloud semantic segmentation. Most previous aggregation methods are based on sampling a representative point subset, i.e., by a carefully designed point density metric, facing expensive computation cost especially for large-scale point clouds. Even though speeding up the point sampling process is studied by several recent works, but the component points in the sampled subset are uncertain and may change randomly, thus leading to corrupted geometric structure and discarded edges for representing an object. Therefore, we propose the DCNet, which consists of a fast point random sampling based encoder-decoder structure and several fully connected layers for semantic segmentation. To overcome the key feature loss caused by random down-sampling, the DCNet develops two novel local feature aggregation schemes: Double attention and Consistent constraints, to learn features that are discriminative for the challenging scenarios as above. The former considers both topological and semantic similarity of neighboring points to generate attention features for discriminating classes with similar geometric structures. The latter develops class-consistent constraints between adjacent layers in the decoder stage, to guide each point to aggregate with high-level semantic features of points belonging to the same class from the previous layer, which is beneficial for distinguishing neighboring points of the same class on the boundary. We conduct experiments and compare the proposed DCNet with existing methods on two benchmarks S3DIS and Semantic3D. Experiments show that the mean Intersection-over-Union (mIoU) of our method outperforms state-of-the-art methods by 2-3%, based on the same fast random sampling, and is also comparable to latest sampling-slower but accuracy-higher methods. That is, our method achieves the optimal speed-accuracy trade-off in the field of point cloud segmentation. 

DCNet: Large-scale Point Cloud Semantic Segmentation with Discriminative and Efficient Feature Aggregation

In recent years, point clouds have been widely used in powerline inspection, smart cities, autonomous driving, and other fields. The deep learning-based point cloud processing methods have attracted more and more attention due to the developments of laser scanning technology and machine learning. However, the recent methods largely ignore global contextual relationships and do not make full use of the complementation between local feature and high-level geometric information. To the problem, we propose a novel deep neural network, namely, the Dense connection-based Kernel Point Network (DenseKPNet), which can greatly expand the receptive field of kernel point convolution to extract rich semantic context information and valuable geometric features from the local region effectively. Specifically, we first design a multiscale convolution kernel point module to extract initial geometric features from coarse to fine. Then, we design the dense connection module to efficiently learn more expressive local geometric features while capturing rich contextual information. In addition, we propose the kernel point convolution attention module (KPCAM), which can capture global interdependencies between points and strengthen the discriminativeness of effective features. We evaluate our method on public indoor and outdoor datasets. The qualitative and quantitative experimental results show the effectiveness of DenseKPNet. The mIoU of the proposed method on S3DIS and semantic3D datasets can reach 68.9% and 77.9%, respectively.

DenseKPNET: Dense Kernel Point Convolutional Neural Networks for Point Cloud Semantic Segmentation

The accuracy with which a neural network interprets a point cloud depends on the quality of the features expressed by the network. Addressing this issue, we propose a multi-level feature extraction layer (MFEL) which collects local contextual feature and global information by modeling point clouds at different levels. The MFEL is mainly composed of three independent modules, including the aggregated GAPLayer, the spatial position perceptron, and the RBFLayer, which learn point cloud features from three different scales. The aggregated GAPLayer aggregates the geometry features of neighboring points in a local coordinate system to centroid by graph convolution. Then, the spatial position perceptron independently learns the position features of each point in the world coordinate system. Finally, the RBFLayer aggregates points into pointsets according to the correlation between features, and extracts features from the pointset scale through the quantization layer. Based on the MFEL, an end-to-end classification and segmentation network, namely the MFNet and MFNet-S, is proposed. In the proposed network, the channel-attention mechanism is employed to better aggregate multi-level features. We conduct classification and semantic segmentation experiments on four standard datasets. The results show that the proposed method outperforms the compared methods on the multiple datasets, resulting in 93.1% classification accuracy in ModelNet40. Furthermore, the mIoU of part semantic segmentation in ShapeNet is 85.4%, and the mIoU for semantic segmentation in S3DIS and Semantic3D is 62.9% and 71.9%, respectively.

/pdf/mfnet-multi-level-feature-extraction-and-fusion-network-for-3cg7ji8y.pdf

MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification

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DenseKPNET: Dense Kernel Point Convolutional Neural Networks for Point Cloud Semantic Segmentation

MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification