Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds

Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. Due to the unstructured nature of point clouds, designing deep neural architectures for point cloud semantic segmentation is often not straightforward. In this work, we circumvent this problem by devising a technique to exploit structured neural architectures for unstructured data. In particular, we employ the popular convolutional neural network (CNN) architectures to perform semantic segmentation of LiDAR data. We propose a projection-based scheme that performs an angle-wise slicing of large 3D point clouds and transforms those slices into 2D grids. Accounting for intensity and reflectivity of the LiDAR input, the 2D grid allows us to construct a pseudo image for the point cloud slice. We enhance this image with low-level image processing techniques of normalization, histogram equalization, and decorrelation stretch to suit our ultimate object of semantic segmentation. A large number of images thus generated are used to induce an encoder-decoder CNN model that learns to compute a segmented 2D projection of the scene, which we finally back project to the 3D point cloud. In addition to a novel method, this article also makes a second major contribution of introducing the enhanced version of our large-scale public PC-Urban outdoor dataset which is captured in a civic setup with an Ouster LiDAR sensor. The updated dataset (PC-Urban_V2) provides nearly 8 billion points including over 100 million points labeled for 25 classes of interest. We provide a thorough evaluation of our technique on PC-Urban_V2 and three other public datasets.

Semantic segmentation of large-scale point clouds by integrating attention mechanisms and transformer models

DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds

Point cloud has emerged as the most popular three-dimensional (3D) data format in recent years for several scientific and industrial applications. Point cloud semantic segmentation has piqued the researcher’s interest, which is a crucial stage in 3D analysis and scene comprehension. Deep learning-based processing is more feasible to increase the availability of point cloud acquisition tools that is LiDAR systems at the user end. The point cloud learning achieves tremendous success in object detection, object categorization, and semantic segmentation. To summarize the recent works with chronological development, comprehensive review of projection-, voxel-, and direct point-based point cloud semantic segmentation methods is performed from various perspectives. The commonly used point cloud benchmark datasets with their characteristics are discussed, and they are used for the performance analysis and comparison of several state-of-the-art segmentation methods. The quantitative performance analysis of these deep learning models summarizes the trend of semantic segmentation of point clouds. In the context of point cloud semantic segmentation, the various methods have specific roles. Based on the review of methods working and their performance analysis, it is concluded that the projection-based methods prioritize efficiency, which is ideal in unavailability of high-performance computing system. Voxel-based methods capture overall context, serving well in 3D object classification. Point-based approaches excel in fine details and efficiency, suited for tasks like 3D semantic segmentation. Choosing the suitable method depends on the task, data, and resources. KPConv and DGCNN are popular choices, especially for precision and adaptability to point density. However, method performance varies, underlining the need for tailored selection. Hybrid approaches, combining method strengths, promise superior results.

LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment

Point cloud data provide three-dimensional (3D) information about objects in the real world, containing rich semantic features. Therefore, the task of semantic segmentation of point clouds has been widely applied in fields such as robotics and autonomous driving. Although existing research has made unprecedented progress, achieving real-time semantic segmentation of point clouds on airborne devices still faces challenges due to excessive computational and memory requirements. To address this issue, we propose a novel sequence convolution semantic segmentation architecture that integrates Convolutional Neural Networks (CNN) with a sequence-to-sequence (seq2seq) structure, termed SeqConv-Net. This architecture views point cloud semantic segmentation as a sequence generation task. Based on our unique perspective of spatially ordered sequences, we use Recurrent Neural Networks (RNN) to encode elevation information, then input the structured hidden states into a CNN for planar feature extraction. The results are combined with the RNN’s encoded outputs via residual connections and are fed into a decoder for sequence prediction in a seq2seq manner. Experiments show that the SeqConv-Net architecture achieves 75.5% mean Intersection Over Union (mIOU) accuracy on the DALES dataset, with the total processing speed from data preprocessing to prediction being several to tens of times faster than existing methods. Additionally, SeqConv-Net can balance accuracy and speed by adjusting the hyperparameters and using different RNNs and CNNs, providing a new solution for real-time point cloud semantic segmentation in airborne environments.

Point cloud segmentation neural network with same-type point cloud assistance

Three-dimensional (3D) semantic segmentation of point clouds is important in many scenarios, such as automatic driving, robotic navigation, while edge computing is indispensable in the devices. Deep learning methods based on point sampling prove to be computation and memory efficient to tackle large-scale point clouds (e.g. millions of points). However, some local features may be abandoned while sampling. In this paper, We present one end-to-end 3D semantic segmentation framework based on dilated nearest neighbor encoding. Instead of down-sampling point cloud directly, we propose a dilated nearest neighbor encoding module to broaden the network’s receptive field to learn more 3D geometric information. Without increase of network parameters, our method is computation and memory efficient for large-scale point clouds. We have evaluated the dilated nearest neighbor encoding in two different networks. The first is the random sampling with local feature aggregation. The second is the Point Transformer. We have evaluated the quality of the semantic segmentation on the benchmark 3D dataset S3DIS, and demonstrate that the proposed dilated nearest neighbor encoding exhibited stable advantages over baseline and competing methods.

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc.

A shape-based segmentation method for mobile laser scanning point clouds

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

Benchmarking tree instance segmentation of terrestrial laser scanning point clouds

BushNet: Effective semantic segmentation of bush in large-scale point clouds

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Comparative experiments show that our RandLA-Net can process 1 million points in a single pass up to 200× faster than existing approaches. Moreover, extensive experiments on five large-scale point cloud datasets, including Semantic3D, SemanticKITTI, Toronto3D, NPM3D and S3DIS, demonstrate the state-of-the-art semantic segmentation performance of our RandLA-Net.

Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods.