SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

Zhao, Lin; Shi, Xu; Liu, Liman; Ming, Delie; Tao, Wenbing

doi:10.3390/rs14184471

Cited by 23 publications

(14 citation statements)

References 83 publications

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“…SVASeg [255] utilizes Sparse Voxel-based Attention (SVHA) to capture long-range dependencies between sparse points in point clouds. SVHA module points into local regions, computes attention weights, and aggregates features from neighboring regions to predict semantic labels.…”

Section: Sparse Discretization Representationmentioning

confidence: 99%

“…In Table 5, the results reveal that among discretizationbased methods, Cylinder3D [258], SVASeg [255], and MS1_DVS [259] emerge as the top performers in SemanticKITTI, nuScenes, and Semantic3D (reduced) datasets. However, across various methodologies, MS1_DVS [259] and Swin3D-L [256] excel, surpassing all other approaches in the Semantic3D SanNet, S3DIs (area-5 and 6-fold) datasets.…”

Section: Sparse Discretization Representationmentioning

confidence: 99%

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A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

Sarker,

Stone

et al. 2024

Machine Vision and Applications

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Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-namely, 3D shape classification and semantic segmentation.

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Section: Sparse Discretization Representationmentioning

confidence: 99%

Section: Sparse Discretization Representationmentioning

confidence: 99%

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

Sarker,

Stone

et al. 2024

Machine Vision and Applications

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show abstract

“…It achieves large improvements in accuracy and substantially reduces catastrophic failures. Sparse voxel-based networks have been applied for the efficient processing of point clouds and voxels [36][37][38]. SVR-Net contains an iterative pipeline that is similar to DROID-SLAM, but it estimates TSDFs, instead of depth values, with a sparse voxelized structure for direct mapping.…”

Section: Related Workmentioning

confidence: 99%

SVR-Net: A Sparse Voxelized Recurrent Network for Robust Monocular SLAM with Direct TSDF Mapping

Lang

Fan

Chang

2023

Sensors

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Simultaneous localization and mapping (SLAM) plays a fundamental role in downstream tasks including navigation and planning. However, monocular visual SLAM faces challenges in robust pose estimation and map construction. This study proposes a monocular SLAM system based on a sparse voxelized recurrent network, SVR-Net. It extracts voxel features from a pair of frames for correlation and recursively matches them to estimate pose and dense map. The sparse voxelized structure is designed to reduce memory occupation of voxel features. Meanwhile, gated recurrent units are incorporated to iteratively search for optimal matches on correlation maps, thereby enhancing the robustness of the system. Additionally, Gauss–Newton updates are embedded in iterations to impose geometrical constraints, which ensure accurate pose estimation. After end-to-end training on ScanNet, SVR-Net is evaluated on TUM-RGBD and successfully estimates poses on all nine scenes, while traditional ORB-SLAM fails on most of them. Furthermore, absolute trajectory error (ATE) results demonstrate that the tracking accuracy is comparable to that of DeepV2D. Unlike most previous monocular SLAM systems, SVR-Net directly estimates dense TSDF maps suitable for downstream tasks with high efficiency of data exploitation. This study contributes to the development of robust monocular visual SLAM systems and direct TSDF mapping.

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“…In view of the impact of sparse characteristics of point cloud on semantic segmentation, some scholars have carried out relevant studies. Here, SVASeg is a typical sparse voxelbased attention network, used to semantic segmentation [26]. This method captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA).…”

Section: Introductionmentioning

confidence: 99%

A Graph Aggregation Convolution and Attention Mechanism Based Semantic Segmentation Method for Sparse Lidar Point Cloud Data

Zheng,

Chen,

Feng

et al. 2024

IEEE Access

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In recent years, following the development of sensor and computer techniques, it is favored by many fields, i.e. automatic drive, intelligent home, etc., which the deep learning based semantic segmentation method for point cloud data collected by LiDAR. This type method can automatic extract features of point cloud, helping label semantic categories. However, compared to 2D images, 3D point cloud data is more expensive to acquire. Hence, to save research and production costs, the low-thread LiDAR is a good choice. For one observation scenario, following the decrease of the line, the point cloud will become sparse, which may cause the information loss. To balance the cost and the segmentation effect, we provide a point cloud completion auxiliary semantic segmentation method. The baseline of the proposed method is Bilateral Augmentation and Adaptive Fusion (BAAF) model. The main contribution is a completion module introduction in feature extraction part of BAAF for point cloud data reconstruction. Under the premise of using low-thread LiDAR sensor to collect data, the semantic segmentation effect of 3D field point cloud is improved as much as possible. It provides theoretical basis for cost saving in practical industrial application. The feature extraction of completion module consists of Graph Aggregation Convolution (GAC) and attention mechanism. Then, we use shuffle transform to upsampling data. In addition, to analyze the effectiveness of the proposed method, we make a new dataset with sparse point cloud data, i.e. Sparse-SemanticKITTI dataset, based on public SemanticKITTI dataset. Furthermore, in experiment part, we prove the research significance. Moreover, we compare the segmentation results between classical methods to the proposed one based on point cloud data in SemanticKITTI, Sparse-SemanticKITTI and Semantic3D dataset, respectively. The effectiveness of the proposed one is obvious. Finally, the model complexity is analyzed. In sum, we provide a sparse point cloud semantic segmentation method to balance the cost and the effect.

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SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

Cited by 23 publications

References 83 publications

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

SVR-Net: A Sparse Voxelized Recurrent Network for Robust Monocular SLAM with Direct TSDF Mapping

A Graph Aggregation Convolution and Attention Mechanism Based Semantic Segmentation Method for Sparse Lidar Point Cloud Data

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