Fast point cloud segmentation based on flood-fill algorithm

Chu, Phuong Minh; Cho, Seoungjae; Park, Yong Woon; Cho, Kyungeun

doi:10.1109/mfi.2017.8170397

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…Thus, we resort to the volumetric grids as an intermediate representation to acquire the occupancy field for each part shape. Specifically, the whole object is first voxelized followed by a flood‐fill algorithm [CCPC17] to achieve solid voxlization. We then sample points on the mesh surface of the object parts.…”

Section: Methodsmentioning

confidence: 99%

Learning Part Generation and Assembly for Sketching Man‐Made Objects

Zhu

Nie

et al. 2020

Computer Graphics Forum

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Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.

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Section: Methodsmentioning

confidence: 99%

Learning Part Generation and Assembly for Sketching Man‐Made Objects

Zhu

Nie

et al. 2020

Computer Graphics Forum

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“…As mentioned above, the nonground points are obtained after applying the segmentation algorithm. In the first step, the nonground points are segmented into separate objects based on a flood-fill algorithm proposed elsewhere [37]. In the second step, a fast object tracking algorithm [38] is applied to obtain a list of dynamic objects.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, DATMO systems are time-consuming because of the high computation complexity. Therefore, in this study, we employed our previous DATMO system [36][37][38] to detect dynamic objects and collect points on the dynamic objects in previous frames. In addition, because of the dynamic environment, an algorithm is required to eliminate the traces of dynamic objects.…”

Section: Related Workmentioning

confidence: 99%

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

et al. 2018

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Nowadays, unmanned ground vehicles (UGVs) are widely used for many applications. UGVs have sensors including multi-channel laser sensors, two-dimensional (2D) cameras, Global Positioning System receivers, and inertial measurement units (GPS-IMU). Multi-channel laser sensors and 2D cameras are installed to collect information regarding the environment surrounding the vehicle. Moreover, the GPS-IMU system is used to determine the position, acceleration, and velocity of the vehicle. This paper proposes a fast and effective method for modeling nonground scenes using multiple types of sensor data captured through a remote-controlled robot. The multi-channel laser sensor returns a point cloud in each frame. We separated the point clouds into ground and nonground areas before modeling the three-dimensional (3D) scenes. The ground part was used to create a dynamic triangular mesh based on the height map and vehicle position. The modeling of nonground parts in dynamic environments including moving objects is more challenging than modeling of ground parts. In the first step, we applied our object segmentation algorithm to divide nonground points into separate objects. Next, an object tracking algorithm was implemented to detect dynamic objects. Subsequently, nonground objects other than large dynamic ones, such as cars, were separated into two groups: surface objects and non-surface objects. We employed colored particles to model the non-surface objects. To model the surface and large dynamic objects, we used two dynamic projection panels to generate 3D meshes. In addition, we applied two processes to optimize the modeling result. First, we removed any trace of the moving objects, and collected the points on the dynamic objects in previous frames. Next, these points were merged with the nonground points in the current frame. We also applied slide window and near point projection techniques to fill the holes in the meshes. Finally, we applied texture mapping using 2D images captured using three cameras installed in the front of the robot. The results of the experiments prove that our nonground modeling method can be used to model photorealistic and real-time 3D scenes around a remote-controlled robot.

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“…After labeling the nonground points, they are clustered by the flood-fill algorithm [27]. The flood-fill algorithm is composed of two steps.…”

Section: B Roi Produced By Flood-fill Algorithmmentioning

confidence: 99%

3D Point Cloud Classification for Autonomous Driving via Dense-Residual Fusion Network

et al. 2020

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Compared with the state-of-the-art architectures, using the 3D point cloud as the input of the 2D convolutional neural network without preprocessing will restrict the feature expression of the network. To address this issue, we propose a high-precision classification network using bearing angle (BA) images, depth images, and RGB images. Due to the development of unmanned vehicles, determining how to recognize objects from the information collected by sensors is important. Our approach takes data from LiDAR and a camera and projects a 3D point cloud into 2D BA images and depth images. The RGB image captured by the camera is used to select the region of interest (ROI) corresponding to the point cloud. However, only adding input information is not enough to improve the classification ability of general convolutional neural networks. In our approach, we use a Dense-Residual Fusion Network (DRF-Net), which consists of Dense-Residual Blocks (DRBs). The Dense-Residual Fusion Network can achieve 97.92% accuracy with three input formats on a KITTI raw dataset. INDEX TERMS Object classification, 3D point cloud, Convolution neural network

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Fast point cloud segmentation based on flood-fill algorithm

Cited by 16 publications

References 10 publications

Learning Part Generation and Assembly for Sketching Man‐Made Objects

Learning Part Generation and Assembly for Sketching Man‐Made Objects

Multimedia System for Real-Time Photorealistic Nonground Modeling of 3D Dynamic Environment for Remote Control System

3D Point Cloud Classification for Autonomous Driving via Dense-Residual Fusion Network

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