Dr. Hongkai Ding Profile

Dr. Hongkai Ding

at East China Univ of Science And Technology

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Publications (2)

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Proceedings Article | 13 June 2024 Paper

Geometry reconstruction and fusion for large-scale, featureless, and specular surfaces

Hongkai Ding, Jianjun Yi

Proceedings Volume 13180, 131801K (2024) https://doi.org/10.1117/12.3033553

KEYWORDS: Calibration, Cameras, Feature fusion, Wavefront reconstruction, Imaging systems, Point clouds, Mirrors, Mirror surfaces, Reconstruction algorithms, Stereoscopic cameras

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3D reconstruction is greatly important for perceptual understanding of applications in robotics, computer graphics, and human-computer interaction. Though tremendous researches have been carried out for geometry reconstruction, there are still many challenges to cope with the large-scale, featureless, and specular surfaces. In this paper, we are motivated to propose a unified framework of geometry reconstruction and fusion for surfaces with the above characteristics. For the geometry reconstruction of specular surfaces, we present a method combining Stereo Phase Measuring Deflectometry (SPMD) with modal wavefront reconstruction to retrieve a reliable geometric surface. For the geometry fusion of largescale featureless surfaces, we put forward an offline calibration approach exploiting the Structure-from-Motion (SfM) to merge point clouds reconstructed from different viewpoints. To evaluate the effectiveness of our method, we build a specific SPMD camera system, design a large-scale calibration board, and set up a test scene with an industrial robot and car body. The experiments show that our method can achieve a good performance in terms of reconstruction accuracy of around 0.02mm and fusion accuracy of around 2mm for a large surface.

Proceedings Article | 4 January 2021 Paper

Automated synthetic datasets construction for part semantic segmentation of non-cooperative satellites

Hongkai Ding, Jianjun Yi, Zhuoran Wang, Yajun Zhang, Hailei Wu, Shuqing Cao

Proceedings Volume 11605, 116050X (2021) https://doi.org/10.1117/12.2586906

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Part semantic segmentation based on deep learning provides a new insight for accurate vision understanding of noncooperative satellite as well as for further on-orbit servicing tasks like inspection, repair, and close-proximity robotic manipulation. However, carrying out such researches requires a tremendous amount of data, which is extremely hard and expensive in space. Moreover, the manual annotation for fine-grained tasks like segmentation will cost a lot of labor. Thus, in this paper, we present an efficient method of automated synthetic datasets construction for part-level segmentation of non-cooperative satellite, which is capable of generating thousands of multi-source data (RGB image and point cloud) and the corresponding high-quality annotation. Specifically, the Fibonacci lattice is used for multiple viewpoints sampling of the virtual camera to capture RGB-D images. A trick of segmentation of the customized image in HSV color space is applied to get labels automatically. Furthermore, we employ several data augmentation techniques to expand and diversify the datasets, which improves the generalization of the algorithm. Finally, we carry out the case study using the pointnet++ network based on our generated point cloud data, to validate the feasibility and effectiveness of our method.

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