Local Transformer Network on 3D Point Cloud Semantic Segmentation

RGAM: A novel network architecture for 3D point cloud semantic segmentation in indoor scenes

Aiming at the problem that the relationship between geometric features and semantic features is ignored in the point cloud data downsampling process, which leads to inaccurate segmentation of object boundaries and structural details, this paper proposes a 3D point cloud semantic segmentation network based on multi-scale dense nested type. Firstly, a dense nested network architecture is constructed by nesting multiple multi-scale feature fusion modules to fuse multi-scale features of different directions between encoder-decoder paths, so as to effectively propagate local ge-ometric context information and enhance the ability of cross-scale information interaction. Secondly, a local feature aggregation unit is constructed in the multi-scale feature fusion module, which strengthens the structural awareness within the local point set based on graph convolution and attention mechanism, and promotes the complementarity of local geometric features and abstract semantic information. Then, the cross-layer multi-loss supervision module is combined to further optimize the multi-scale feature propagation, which makes the network training more stable and improves the point cloud segmentation accuracy. Finally, this paper verified the proposed network on the S3DIS dataset. The experimental results show that the proposed network has the mean intersection over Union of 71.2% and the overall accuracy of 88.7%, which is 1.2 and 0.7 percentage points higher than RandLA-Net, respectively, which proves that the proposed network can effectively improve the accuracy of 3D point cloud semantic segmentation.

In this paper, a Backward Attentive Fusing Network with Local Aggregation Classifier (BAF-LAC) is proposed to improve the performance of 3D point cloud semantic segmentation. It consists of a Backward Attentive Fusing Encoder-Decoder (BAF-ED) to learn semantic features and a Local Aggregation Classifier (LAC) to maintain the context-awareness of points. BAF-ED narrows the semantic gap between the encoder and the decoder via fusing multi-layer encoder features with the decoder features. High-level encoder features are transformed into an attention map to modulate low-level encoder features backward. LAC adaptively enhances the intermediate features in point-wise MLPs via aggregating the features of neighboring points into the center point. It takes the place of commonly used post-processing techniques and retains context consistency into the classifier. Equipped with these modules, BAF-LAC can extract discriminative semantic features and predict smoother results. Extensive experiments on Semantic3D, SemanticKITTI, and S3DIS demonstrate that the proposed method can achieve competitive results against the state-of-the-art methods.

Unsupervised domain adaptation (UDA) is increasingly used for 3D point cloud semantic segmentation tasks due to its ability to address the issue of missing labels for new domains. However, most existing unsupervised domain adaptation methods focus only on uni‐modal data and are rarely applied to multi‐modal data. Therefore, we propose a cross‐modal UDA on multi‐modal datasets that contain 3D point clouds and 2D images for 3D Semantic Segmentation. Specifically, we first propose a Dual discriminator‐based Domain Adaptation (Dd‐bDA) module to enhance the adaptability of different domains. Second, given that the robustness of depth information to domain shifts can provide more details for semantic segmentation, we further employ a Dense depth Feature Fusion (DdFF) module to extract image features with rich depth cues. We evaluate our model in four unsupervised domain adaptation scenarios, i.e., dataset‐to‐dataset (A2D2 → SemanticKITTI), Day‐to‐Night, country‐to‐country (USA → Singapore), and synthetic‐to‐real (VirtualKITTI → SemanticKITTI). In all settings, the experimental results achieve significant improvements and surpass state‐of‐the‐art models.

3D point cloud semantic segmentation is a fundamental task for 3D scene understanding. However, most existing pipelines usually use k-NN or ball query operation to form hard neighborhoods, which may cross different semantic objects, resulting low-quality local features. To address this issue, we propose a multi-scale superpoint network that gradually generates multi-scale soft neighborhoods to extract geometric local features, thereby boosting the 3D semantic segmentation performance. Specifically, we present a simple yet efficient superpoint merging module that merge small-scale superpoints to obtain large-scale superpoint by considering the feature similarity of superpoints, so that we can obtain multi-scale geometric features of point clouds. We also develop a superpoint upsampling module that adopt inverse mapping function to propagate multi-scale features from low-resolution point cloud to high-resolution point cloud. By integrating our multi-scale superpoint network into a simple point based semantic segmentation network, our method can obtain SOTA results on S3DIS Area 5 and 6-fold, and competitive results on ScanNet v2.

Improving performance of deep learning models for 3D point cloud semantic segmentation via attention mechanisms

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.

Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previous efficient state-of-the-art methods relied on 2D spherical projection of point clouds as input for 2D fully convolutional neural networks to balance the accuracy-speed trade-off. This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud to mitigate the loss of information inherent in single projection methods. Our Multi-Projection Fusion (MPF) framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models then combines the segmentation results of both views. The proposed framework is validated on the SemanticKITTI dataset where it achieved a mIoU of 55.5 which is higher than state-of-the-art projection-based methods RangeNet++ [23] and PolarNet [44] while being 1.6x faster than the former and 3.1x faster than the latter.

In recent years, the numerous advantages introduced by Building Information modelling (BIM) have led in its application on the heritage environment and giving birth to the concept of H-BIM (Heritage BIM). The resulting demand in heritage survey data processing has focused this research on the development of strategies and methods to improve the construction of three-dimensional and informative models starting from 3D point clouds. The implementation of an automated procedure is fundamental for easing and speeding up the survey data processing and one of the most challenging tasks to achieve this purpose is the problem of semantic segmentation. The research presented in this paper aims at testing already existing methods and exploring new strategies for 3D point cloud semantic segmentation on heritage scenarios focusing on deep learning and neural network techniques.

Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-to-end dual-task segmentation network designed for effective and precise semantic labeling and instance recognition of tomato point clouds, based on the Pheno4D dataset. TSINet adopts an encoder–decoder architecture, where a shared encoder incorporates four Geometry-Aware Adaptive Feature Extraction Blocks (GAFEBs) to effectively capture local structures and geometric relationships in raw point clouds. Two parallel decoder branches are employed to independently decode shared high-level features for the respective segmentation tasks. Additionally, a Dual Attention-Based Feature Enhancement Module (DAFEM) is introduced to further enrich feature representations. The experimental results demonstrate that TSINet achieves superior performance in both semantic and instance segmentation, particularly excelling in challenging categories such as stems and large-scale instances. Specifically, TSINet achieves 97.00% mean precision, 96.17% recall, 96.57% F1-score, and 93.43% IoU in semantic segmentation and 81.54% mPrec, 81.69% mRec, 81.60% mCov, and 86.40% mWCov in instance segmentation. Compared with state-of-the-art methods, TSINet achieves balanced improvements across all metrics, significantly reducing false positives and false negatives while enhancing spatial completeness and segmentation accuracy. Furthermore, we conducted ablation studies and generalization tests to systematically validate the effectiveness of each TSINet component and the overall robustness of the model. This study provides an effective technological approach for high-throughput automated phenotyping of tomato plants, contributing to the advancement of intelligent agricultural management.

As a pivotal task in 3D scene understanding, 3D point cloud semantic segmentation is often challenged by the presence of mislabeled data in real-world annotations. Such noisy labels can mislead the segmentation model into learning erroneous features during training, thereby degrading its performance. To address this issue, this paper proposes a robust training framework for 3D semantic segmentation based on noisy label correction. The proposed method does not rely on prior knowledge of point cloud density and is capable of learning robust segmentation models under various noise rates. Our approach consists of two core components: a Density-Adaptive Neighborhood Clustering algorithm that dynamically adjusts the neighborhood radius and minimum point count according to local point density variations, and a Dynamically-Weighted Label Refurbishing mechanism, which rectifies noisy labels at the cluster level based on historical prediction confidence. Extensive experiments on the S3DIS dataset demonstrate that our method outperforms existing baselines and state-of-the-art (SOTA) approaches under symmetric, asymmetric, and mixed noise settings. Notably, it maintains strong robustness even under high noise ratios. Furthermore, our method integrates label structures and stores them in a vectorized format, significantly reducing data transfer overhead between CPU and GPU and mitigating resource wastage. This design makes the approach particularly efficient in resource-constrained environments such as cloud servers, where CPU performance is often a bottleneck. As a result, our method achieves a notable improvement in training efficiency, with each training epoch requiring only approximately one-tenth of the time compared to current SOTA methods.

Three-dimensional (3D) semantic segmentation based on point clouds has become a critical technology in the field of intelligent spatial perception and scene understanding. However, most existing methods cannot fully exploit the spatial feature of 3D point cloud data and the orderliness of two-dimensional (2D) image information, resulting in an inability to effectively improve segmentation accuracy. This study proposes a novel 2D image and 3D point cloud fusion method named shared multi-layer perceptron fusion semantic segmentation (SMFusionSeg) for semantic segmentation. The rich features of 2D image segmentation and spatial information of 3D point clouds are integrated by designing a fusion architecture that facilitates the transfer of 2D semantics to 3D semantics. Specifically, the fusion architecture concatenates the features from both 2D and 3D domains, which are then further integrated through a shared multi-layer perceptron (MLP) and optimized using an attention mechanism to enhance feature effectiveness and distinctiveness. Moreover, a knowledge distillation strategy is introduced to improve the learning capability and segmentation accuracy in parsing complex scenes. Experimental results on the SemanticKITTI dataset demonstrate that the proposed SMFusionSeg achieves a mean Intersection over Union ( mIoU ) of 65.4%, significantly outperforming traditional single-modal methods and many existing segmentation techniques, thereby effectively enhancing the precision and robustness of geographic feature recognition.

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.

Few-Shot 3D Point Cloud Semantic Segmentation (3D-FS) mitigates the issues of insufficient data annotation and emerging novel classes in real-world scenarios, but it totally ignores the performance on base classes. In this paper, we address a more practical task, Generalized Few-Shot 3D Point Cloud Semantic Segmentation (3D-GFS), which aims to perform segmentation simultaneously on base classes with adequate samples and novel classes with few samples. Based on the prototypical Base Model, we propose Adaptive Support Enrichment module and Query Aware Representation module to utilize the contextual information of semantic segmentation. The former exploits the co-relationship between base and novel classes in support samples while the latter mines semantic information from query samples. Besides, considering the different embedding spaces, we propose a new training strategy to get a better representation of prototypes. Experiments on S3DIS and ScanNet show that our proposed method outperforms our Base Model and the conventional 3D-FS methods.

LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning. LiDAR-based point cloud semantic segmentation is an important task with a critical real-time requirement. However, most of the existing convolutional neural network (CNN) models for 3D point cloud semantic segmentation are very complex and can hardly be processed at real-time on an embedded platform. In this study, a lightweight CNN structure was proposed for projection-based LiDAR point cloud semantic segmentation with only 1.9 M parameters that gave an 87% reduction comparing to the state-of-the-art networks. When evaluated on a GPU, the processing time was 38.5 ms per frame, and it achieved a 47.9% mIoU score on Semantic-KITTI dataset. In addition, the proposed CNN is targeted on an FPGA using an NVDLA architecture, which results in a 2.74x speedup over the GPU implementation with a 46 times improvement in terms of power efficiency.