A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

Wang, Wei; Zhang, Yi; Ge, Genyu; Jiang, Qin; Wang, Yang; Li-he, HU

doi:10.3390/app11209581

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…For the given 3D scene and viewing angle, the occlusion relationships could be judged and the invisible graphical objects will be discarded so that it can reduce the complexity and improve the realism for the whole scene, thereby realizing the low-load creation and network transmission, which is shown in Fig. 1 below [17].…”

Section: Occlusion Cullingmentioning

confidence: 99%

3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline

Ye¹,

Liu²,

Chen³

et al. 2023

Computer Systems Science and Engineering

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Nowadays, Web browsers have become an important carrier of 3D model visualization because of their convenience and portability. During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS (Frames Per Second), occlusion culling, as an important method for rendering optimization, can remove most of the occluded objects and improve rendering efficiency. The traditional occlusion culling algorithm (TOCA) is calculated by traversing all objects in the scene, which involves a large amount of repeated calculation and time consumption. To advance the rendering process and enhance rendering efficiency, this paper proposes an occlusion culling with three different optimization methods based on the WebGPU Computing Pipeline. Firstly, for the problem of large amounts of repeated calculation processes in TOCA, these units are moved from the CPU to the GPU for parallel computing, thereby accelerating the calculation of the Potential Visible Sets (PVS); Then, for the huge overhead of creating pipeline caused by too many 3D models in a certain scene, the Breaking Occlusion Culling Algorithm (BOCA) is introduced, which removes some nodes according to building a Hierarchical Bounding Volume (BVH) scene tree to reduce the overhead of creating pipelines; After that, the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally, the PVS is obtained by parallel computing. In the experiments, 3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing. The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively, with 97% rendering efficiency improvement compared with BOCA, thereby accelerating the rendering process on Web browsers.

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Section: Occlusion Cullingmentioning

confidence: 99%

3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline

Ye¹,

Liu²,

Chen³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…Octree and 3D KD-tree [37] are often used in 3D space scene management, such as space division of point clouds, fast search of nearby points, etc. [38,39]. The nearest neighbor search processing is in Section 3.3 for finding feature point correspondence.…”

Section: (B) Storage Structurementioning

confidence: 99%

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

Yang,

Chen,

Liu

et al. 2023

Forests

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Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 m2, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots.

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“…In recent years, due to the advent of the era of big data and the rapid development of computer graphics cards, the computing power of computers has also improved, which has accelerated the development of artificial intelligence in the computer field. There are more and more studies related to artificial intelligence, for example, the research in [1][2][3][4] has good application value, and the research of object detection has also developed accordingly.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5s Algorithm for Object Detection with an Attention Mechanism

Jiang

Yang

et al. 2022

Electronics

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To improve the accuracy of the You Only Look Once v5s (YOLOv5s) algorithm for object detection, this paper proposes an improved YOLOv5s algorithm, CBAM-YOLOv5s, which introduces an attention mechanism. A convolutional block attention module (CBAM) is incorporated into the YOLOv5s backbone network to improve its feature extraction ability. Furthermore, the complete intersection-over-union (CIoU) loss is used as the object bounding-box regression loss function to accelerate the speed of the regression process. Experiments are carried out on the Pascal Visual Object Classes 2007 (VOC2007) dataset and the Microsoft Common Objects in Context (COCO2014) dataset, which are widely used for object detection evaluations. On the VOC2007 dataset, the experimental results show that compared with those of the original YOLOv5s algorithm, the precision, recall and mean average precision (mAP) of the CBAM-YOLOv5s algorithm are improved by 4.52%, 1.18% and 3.09%, respectively. On the COCO2014 dataset, compared with the original YOLOv5s algorithm, the precision, recall and mAP of the CBAM-YOLOv5s algorithm are increased by 2.21%, 0.88% and 1.39%, respectively.

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A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

Cited by 5 publications

References 33 publications

3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline

3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

An Improved YOLOv5s Algorithm for Object Detection with an Attention Mechanism

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