Segmentation Of LiDAR Point Clouds Research Articles

RPV-CASNet: range-point-voxel integration with channel self-attention network for lidar point cloud segmentation

RPV-CASNet: range-point-voxel integration with channel self-attention network for lidar point cloud segmentation

Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds

Abstract. In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables.

Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

MsVFE and V-SIAM: Attention-based multi-scale feature interaction and fusion for outdoor LiDAR semantic segmentation

The semantic segmentation of outdoor LiDAR point clouds is one of the gigantic fields in the large-scale driving scenario. However, the performances of the state-of-the-art methods are unsatisfactory caused by the intrinsic limitations of the outdoor point clouds with excessive distribution of sparsity and imbalanced distribution of density, both of which become great challenges for a precise segmentation of LiDAR point clouds. To tackle the aforementioned intrinsic problems of point clouds with an improved segmentation performance, we propose a brand new attention-based feature interaction module called Voxel Slicing and Interaction based Attention Module (V-SIAM) that is integrated into the segmentation networks. Our V-SIAM is composed of a Voxel Slicing and Interaction Module (V-SIM) followed by a Voxel Attention Module (VAM), where the V-SIM is utilized to significantly reduce the negative impact caused by the imbalanced point density, in terms of enhancing the interaction of the voxel feature by a novel feature slicing, leading to the enriched voxel feature details of the point clouds. Moreover, the VAM is utilized to reduce the negative effect caused by the excessive sparsity of the point clouds, in terms of recalibration among voxel features via an innovative way, leading to the extraction of adaptive and self-enhanced voxel features. Besides the V-SIAM, a Multi-scale Voxel Feature Extractor (MsVFE), utilized as the preprocessing module of the segmentation networks, is proposed to further alleviate the negative influence caused by the excessive sparsity of the point clouds, realized by fusing the multi-scale voxel information of the sparse point clouds, leading to extraction of more detailed features of the point clouds. Our experimental results show that the proposed methods achieve 73.7% mIoU on the large-scale SemanticKITTI benchmark, outperforming the state-of-the-art PVKD and 2DPass by +1.3% mIoU and +0.8% mIoU, respectively. Moreover, our proposed MsVFE and V-SIAM achieve the state-of-the-art performance on the Toronto3D dataset and KITTI-360 dataset.

PCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation

pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation

Lidar Point Cloud Segmentation Model Based on Improved PointNet++

Lidar Point Cloud Segmentation Model Based on Improved PointNet++

Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial Consistency

Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial Consistency

Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds

We present a post-processing tool for semantic segmentation of Lidar point clouds, called LidarMetaSeg, which estimates the prediction quality segmentwise and classifies prediction errors. For this purpose, we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is an error, i.e., it is a false positive or not. We also train a meta regression model to predict the segmentwise prediction quality in terms of intersection over union. Both models can then be applied to semantic segmentation inferences without knowing the ground truth. In our experiments we use different Lidar segmentation models and datasets and analyze the power of our method. We show that our results outperform other standard approaches based on single uncertainty measures like entropy. Furthermore, we present an in-depth evaluation of our method on predicted classes as well as on predicted categories.

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

GADA-SegNet: gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds

GADA-SegNet: gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds

Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.

Real-Time Semantic Segmentation of LiDAR Point Clouds on Edge Devices for Unmanned Systems

Real-Time Semantic Segmentation of LiDAR Point Clouds on Edge Devices for Unmanned Systems

Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network

Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art.

Fast Positioning Method of Truck Compartment Based on Plane Segmentation

To improve the loading efficiency and shorten the loading time, a method based on LIDAR point cloud segmentation for fast positioning of trucks is proposed for the scenario of level-push automatic loading, in which the truck position and attitude are random. The point cloud data are collected by LIDAR and pre-processed by traditional methods in the order of straight pass filtering, voxel filtering, and plane segmentation. Four planar regions are segmented using the region growth algorithm, and then planar fitting is performed for each of these four regions, which requires a large number of iterations and complex calculations by the traditional planar segmentation method. Focusing on these drawbacks this paper first uses the region growing algorithm to cluster and group the matching points, then combines the least squares and RANSAC algorithms for plane fitting optimization to calculate the intercept of each plane, and finally uses these intercept values to perform spatial geometry calculations to obtain the exact position and locus of the carriage. Experiments show that the method reduces the number of iterations and improves the computational efficiency compared with the traditional RANSAC algorithm, thus improving the efficiency and accuracy of the plane fitting algorithm.

Hierarchical semantic segmentation of urban scene point clouds via group proposal and graph attention network

Although many notable improvements have been devoted to the semantic segmentation of laser scanning (LS) data, the extreme complexity of scanned scenes poses significant challenges in achieving the effective distribution of a category label per point. This study investigates the semantic segmentation of LiDAR point clouds using an improved deep learning method. In particular, the raw data were reorganized based on group proposals using Gaussian learning. We generated a structured multi-scale graph for group proposals, which supports multi-scale analysis in the scale space. Subsequently, a self-adaptive graph convolution network (GCN) was adopted to obtain the best point cloud features. Based on this GCN module, the proposals were semantically labeled by an encoder-decoder network. The proposed level inferences were finally transformed into point-wise predictions. For segmentation result refinement, the output probabilities of the proposed framework were weighted as the input of a developed conditional random field (CRF) algorithm. Experiments with three typical datasets (i.e., ParisLille-3D, Semantic3D, and vKITTI) comprehensively evaluated the performance of our approach. The experimental results demonstrated that the proposed framework can achieve better performance for several objects.

A new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentation

Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.

Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input. By merging multiple frames in the LiDAR sequence as supervision, the optimized SSC module has learned the contextual shape priors from sequential LiDAR data, completing the sparse single sweep point cloud to the dense one. Thus, it inherently improves SS optimization through fully end-to-end training. Besides, a Point-Voxel Interaction (PVI) module is proposed to further enhance the knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of incomplete local geometry of point cloud and complete voxel-wise global structure. Furthermore, the auxiliary SSC and PVI modules can be discarded during inference without extra burden for SS. Extensive experiments confirm that our JS3C-Net achieves superior performance on both SemanticKITTI and SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.

EPointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.

Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation

Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud feature representation and super segmentation performance. First, upon a baseline encoder-decoder architecture for point cloud segmentation, namely, RandLA-Net, an attentive skip connection was proposed to replace the commonly used concatenation to balance the encoder and decoder features of the same scales. Second, a channel attentive enhancement module was introduced to the local attention enhancement module to boost the local feature discriminability and aggregate the local channel structure information. Third, we developed a multi-scale feature aggregation method to capture the global structure of a point cloud from both the encoder and the decoder. The experimental results reported that our MSAAN significantly outperformed state-of-the-art methods, i.e., at least 15.3% mIoU improvement for scene-2 of CSPC dataset, 5.2% for scene-5 of CSPC dataset, and 6.6% for Toronto3D dataset.

Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet

Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet