Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning

Li, Weite; Shigeta, K.; Hasegawa, Kyoko; Li, Liang; Yano, Keiji; Adachi, Motoaki; Tanaka, Satoshi

doi:10.3390/ijgi8090425

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…3D data is growing rapidly in this era. Whether it originated from a human-designed CAD (Computer-Aided Design) model or a scanned point cloud from a LiDAR sensor or an RGB-D camera [10,11]. In addition, most systems acquire 3D directly rather than take images and process them.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

Tham

Hussin

Ismail

2022

IOP Conf. Ser.: Earth Environ. Sci.

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3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

Tham

Hussin

Ismail

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization

Hasegawa

et al. 2021

Remote Sensing

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Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects.

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Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning

Cited by 2 publications

References 21 publications

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization

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