Results 11 to 20 of about 528,144 (284)
Picking Towels in Point Clouds [PDF]
Sensors, 2019 Picking clothing has always been a great challenge in laundry or textile industry automation, especially when some clothes are of the same colors, material and entangled with each other.
Xiaoman Wang +5 more
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PCT: Point cloud transformer [PDF]
Computational Visual Media, 2021 The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image ...
Meng-Hao Guo +5 more
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The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
CoRR, 2021 Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, it is imperative to study the generalization of the convolution networks more closely, especially their robustness under variations in scale and rotations of the input data ...
Xingyi Li +3 more
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Geodesics on Point Clouds [PDF]
Mathematical Problems in Engineering, 2014 We present a novel framework to compute geodesics on implicit surfaces and point clouds. Our framework consists of three parts, particle based approximate geodesics on implicit surfaces, Cartesian grid based approximate geodesics on point clouds, and geodesic correction.
Hongchuan Yu, Jian J. Zhang, Zheng Jiao
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Georeferenced Point Clouds: A Survey of Features and Point Cloud Management [PDF]
ISPRS International Journal of Geo-Information, 2013 This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed.
Johannes Otepka +4 more
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DPDist: Comparing Point Clouds Using Deep Point Cloud Distance [PDF]
, 2020 We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation.
Dahlia Urbach +2 more
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DENOISING OF 3D POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system ...
E. Mugner, N. Seube
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Applied Sciences, 2022 Road high-precision mobile LiDAR measurement point clouds are the digital infrastructures for high-precision maps, autonomous driving, digital twins, etc. High-precision automated semantic segmentation of road point clouds is a crucial research direction.
Peng Cheng +5 more
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A MARKER-FREE CALIBRATION METHOD FOR MOBILE LASER SCANNING POINT CLOUDS CORRECTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Mobile laser scanning systems (MLS) have been widely used in collecting three-dimensional point clouds for many applications, such as 3D mapping, road facilities inventory and high definition map.
B. Yang, Y. Li, X. Zou, Z. Dong
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Point Cloud Audio Processing [PDF]
2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021 Most audio processing pipelines involve transformations that act on fixed-dimensional input representations of audio. For example, when using the Short Time Fourier Transform (STFT) the DFT size specifies a fixed dimension for the input representation. As a consequence, most audio machine learning models are designed to process fixed-size vector inputs
Krishna Subramani, Paris Smaragdis
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