Reconstruction of 3D human motion in real-time using particle swarm optimization with GPU-accelerated fitness function

Kwolek, Bogdan; Rymut, Boguslaw

doi:10.1007/s11554-018-0825-5

Cited by 4 publications

(5 citation statements)

References 43 publications

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“…To compensate for the 3D PC, after deforming the 3D key PCF using the estimated motion vector by the 3D pose estimation, the overlapped PC between the key and target PCFs is removed using the motion-compensated 3D key PCF. Next, the 3D PC is converted to the mesh model for deformation, where we use Poisson surface reconstruction [ 21 ]. Figure 8 is a flow for the mesh deformation to convert mesh using the 3D mesh and skeleton.…”

Section: Temporal Prediction Of Dynamic Point Cloudmentioning

confidence: 99%

Section: Temporal Prediction Of Dynamic Point Cloudmentioning

confidence: 99%

“…In the skeleton hierarchy, the pelvis corresponding to the core is the highest node, and the closer it is to the pelvis, the higher the node is. Figure 9 shows the skeleton hierarchy used [21]. After setting the hierarchy, skinning is performed from the skeleton of the lower hierarchy.…”

Section: Deformation Of 3d Point Cloudmentioning

confidence: 99%

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Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression

Kim

Jang

Lee

et al. 2023

Sensors

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This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

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Section: Temporal Prediction Of Dynamic Point Cloudmentioning

confidence: 99%

Section: Temporal Prediction Of Dynamic Point Cloudmentioning

confidence: 99%

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Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression

Kim

Jang

Lee

et al. 2023

Sensors

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“…Therefore, a speedup could be achieved just by parallelizing this step of the execution. Singh et al use this strategy and state that it is possible to achieve a 10x speedup comparing their implementation (where only the fitness evaluation is performed in a CUDA kernel) to a fully sequential implementation [20,27]. Unfortunately, the authors do not comment on the overhead generated by repeatedly transferring data between CPU and the GPU.…”

Section: Parallelization Of Basic Operationsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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“…In this case, extrinsic calibration is performed only with partial information captured by each camera. When extrinsic calibration is completed, a 3D model is generated by integrating the 3D point cloud using the transformation matrix (3D static registration) [34]. In Figure 6, the 3D static reconstruction algorithm is depicted.…”

Section: D Registrationmentioning

confidence: 99%

3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview

Park

Kim

et al. 2022

Sensors

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This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.

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Reconstruction of 3D human motion in real-time using particle swarm optimization with GPU-accelerated fitness function

Cited by 4 publications

References 43 publications

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression

Parallelization of Swarm Intelligence Algorithms: Literature Review

3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview

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