A survey of dynamic 3D scene reconstruction

doi:10.11996/JG.j.2095-302X.2024010014

Abstract

Abstract:

Three-dimensional reconstruction technology aims to recover the digital 3D representation of an observed scene through sensor input. It is an important research direction in the fields of computer graphics and vision, with significant applications in visualization, simulation, route planning, and various other tasks. Compared to static scenes, dynamic scenes introduce an additional temporal dimension. The reconstruction of dynamic scenes not only requires accurately reconstructing the geometric details of each frame but also capturing the motion trends of the target over time and correlations for downstream analysis tasks, presenting greater challenges to the design of reconstruction algorithms. However, the existing literature pertaining to the reconstruction of dynamic scenes is still in their infancy, and systematic summarizations of existing methodologies are notably lacking. In an endeavor to address these problems and to enlighten future algorithm design, the latest dynamic 3D scene reconstruction technologies in the literature were reviewed and summarized. A general definition of dynamic 3D scene reconstruction and its general solution framework was provided. Existing technologies were reviewed from the perspectives of dynamic 3D representation methods and optimization frameworks, and the reconstruction algorithms and processing methods for structured scenes were discussed. Finally, existing datasets were summarized, the existing problems in dynamic 3D scene reconstruction were identified, and an outlook on future research was provided.

Key words: dynamic 3D reconstruction, literature review, dynamic scene representation, 3D modeling, structured scenes

CLC Number:

TP391

HUANG Jiahui, MU Taijiang. A survey of dynamic 3D scene reconstruction[J]. Journal of Graphics, 2024, 45(1): 14-25.

Figures/Tables 5

Fig. 1 Illustration of different dynamic 3D representations ((a) Voxel grid and deformation field; (b) Spatial-temporal point cloud; (c) Neural implicit field)

Fig. 2 Human reconstruction result of Occupancy Flow[42]

Table 1 Performance comparison of different 3D dynamic reconstruction algorithms

方法名称	发表年份	PSNR
Neural 3D Video^[49]	CVPR 2022	29.6
NeRFPlayer^[51]	TVCG 2023	30.7
StreamRF^[52]	NeurIPS 2022	28.3
HyperReel^[50]	CVPR 2023	31.1

Fig. 3 Qualitative results of dynamic NeRF methods (the first two rows are from HyperReel[50], and the last row is from NeRFPlayer[51], with result on the right and ground truth on the left)

Fig. 4 Results of ClusterVO in indoor and outdoor multibody scenes (upper row: motion segmentation and reconstructed dynamic map of an indoor two-bottle movement scene; lower row: segmentation and reconstructed dynamic map of an outdoor driving scene)

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