Human plays a key role in metaverse and digital twin systems and thus live three-dimensional (3D) human shape reconstruction brings significant improvement for highly accurate anthropomorphic measurements. However, it is still difficult to obtain detailed 3D computational models or “virtual humans” due to the diversity of individual poses and shape deformation. With technical advantages of the metaverse geometric modeling, virtual characters can be created in seconds. Concurrent, tremendous progress has been achieved in 3D modeling technology and larger representative public human datasets have emerged. In this survey, the status quo of 3D body digitization is investigated, and the accompanying processing pipeline is introduced. Specifically, this survey first discusses mainstream 3D scanning technologies for virtual 3D body acquisition. Second, we investigate the status quo of the commonly-used body measurement processing frameworks from four aspects: 1) data acquisition; 2) feature extraction; 3) surface modeling; and 4) landmarking and digital measurements. Finally, through a comparative analysis of prior works, we highlight challenges and new trends of ongoing developments based on insights into the advantages and limitations of existing works.

Object tracking for unmanned aerial vehicle applications in outdoor scenes is a very complex problem. In videos captured by unmanned aerial vehicle, due to frequent variation in illumination, motion blur, image noise, deformation, lack of image texture, occlusion, fast motion, and other degradations, most tracking methods will lead to failure. The article focuses on the object tracking in severely degraded videos. To deal with those various degradations, a real-time object tracking method for high dynamic background is developed. By integrating histogram of oriented gradient, RGB histogram and motion histogram into a novel statistical model, our method can robustly track the target in unmanned aerial vehicle captured videos. Compared to those existing methods, our proposed approach costs less resource in the tracking, significantly increases the tracking speed, and runs faster than state-of-the-art methods. Also, our approach achieved satisfactory tracking results on the challenging visual tracking benchmark, object tracking benchmark 2013, the supplementary experiments demonstrates that our method is more effective and accurate than other methods.

The newly vision-based solid waste sorting systems, a fundamental module in the construction and demolition (CND) waste recycling industry, often fail due to heavily blurred images caused by the vibration of the conveyor belt that carries the solid wastes. Thus, image deblurring is vital. This paper proposes a novel blind deblurring approach in which a penalty function of gradient and image is formulated as the regularization term in the total energy function. This regularization term is based on sparse prior and solved as part of a mathematical optimization problem, which is operated on the dark channel of the input image. Our approach not only reserves the structure information of the image but also avoids over-smoothing in the final restoration. On synthetic and natural blurred images our method outperforms other popular methods. Even with less iterations, the convergence rate and quality of results are superior. We apply this approach in the challenging solid waste image restoration and achieve remarkable results with high validity and reliability.

In this letter, we present a subspace-based nonlocal low-rank tensor approximation framework (SNLRTA) for hyperspectral image (HSI) restoration. The proposed method consists of a subspace learning method to achieve an accurate subspace characterization of HSI and a nonlocal low-rank tensor approximation to take spatial nonlocal self-similarity into consideration. Specifically, the HSI first exploits residual statistics on median filtered image to estimate a robust subspace. Laplacian scale mixture (LSM) modeling is then investigated to model tensor coefficients from overlapping cubes in low-rank subspace. Both the hidden scale parameters and the sparse coefficients therein are adaptively shrink, characterizing the sparsity of similar patches. Meanwhile, the $\ell _{1}$ data fidelity facilitates the implicit detection of outliers after median filtering. Substantiated by extensive experimental results, the proposed method outperforms several state-of-the-art approaches on mixed noise removal, qualitatively and quantitatively.

Estimating the real‐time pose of a free flight aircraft in a complex wind tunnel environment is extremely difficult. Due to the high dynamic testing environment, complicated illumination condition, and the unpredictable motion of target, most general pose estimating methods will fail. In this paper, we introduce a cross‐field of view (FOV) real‐time pose estimation system, which provides high precision pose estimation of the free flight aircraft in the wind tunnel environment. Multiview live RGB‐D streams are used in the system as input to ensure the measurement area can be fully covered. First, a multimodal initialization method is developed to measure the spatial relationship between the RGB‐D camera and the aircraft. Based on all the input multimodal information, a so‐called cross‐FOV model is proposed to recognize the dominating sensor and accurately extract the foreground region in an automatic manner. Second, we develop an RGB‐D‐based pose estimation method for a single target, by which the 3D sparse points and the pose of the target can be simultaneously obtained in real time. Many experiments have been conducted, and an RGB‐D image simulation based on 3D modeling is implemented to verify the effectiveness of our algorithm. Both the real scene’s and simulation scene’s experimental results demonstrate the effectiveness of our method.