Four-Dimensional Scene Reconstruction With Deformable Networks
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Solution Overview
Problem
Existing methods for 4D scene reconstruction struggle to accurately capture dynamic changes and motions in a scene, leading to inadequate model quality and excessive training times, which hampers immersive experiences in virtual and augmented reality applications.
Innovation Solution
A method involving the generation of a three-dimensional scene model from multi-view images, followed by determining a deformable network using camera pose information to create a four-dimensional scene model, incorporating a deformable network to model temporal changes and motions in the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods are used for 4D scene reconstruction, then the process can be completed, but the model quality is inadequate and training time is excessive
Solution Approach 1:
The patent divides the 4D scene reconstruction into two independent stages: first reconstructing the static 3D scene model from multi-view images, then determining the deformable network from the 3D model and multi-view video. This segmentation allows each stage to be optimized independently, improving overall model quality while reducing total training time by avoiding simultaneous optimization of all parameters.
Solution Approach 2:
The patent performs preliminary reconstruction of the static 3D scene model before determining the deformable network for temporal changes. This preliminary action establishes a solid spatial foundation that simplifies the subsequent temporal deformation modeling, leading to better model quality with reduced computational requirements.
2Manufacturing precision
If existing methods are used for 4D scene reconstruction, then the process can be completed, but computational power and storage requirements are excessive
Solution Approach 1:
By segmenting the reconstruction process into static 3D model generation and dynamic deformation modeling stages, the patent reduces the computational burden on each individual stage. The deformable network is determined based on the pre-computed 3D model, which significantly reduces the search space and computational power required compared to simultaneous 4D optimization.
Solution Approach 2:
The patent extracts the spatial structure information into a separate 3D scene model before incorporating temporal dynamics. This extraction allows the deformation network to focus only on temporal changes rather than re-optimizing both spatial and temporal parameters simultaneously, reducing storage requirements and computational energy.
3Reliability
If existing methods are used for 4D scene reconstruction, then the process can be completed, but the ability to capture dynamic changes is inadequate
Solution Approach 1:
The patent segments the modeling of spatial and temporal characteristics into separate stages: first capturing spatial geometry through 3D scene model reconstruction, then capturing temporal dynamics through deformable network determination. This segmentation enables accurate capture of dynamic changes without the complexity of simultaneously optimizing both spatial and temporal parameters.
Solution Approach 2:
The patent introduces a deformable network that models temporal changes as deformations of the static 3D scene model. This dynamic approach allows the system to capture motion and changes in the scene over time by applying temporal deformation fields to the spatial structure, improving capture accuracy while maintaining manageable model complexity.
Data Source
AI summary
Embodiments of the present application disclose a four-dimensional scene reconstruction method and apparatus, and an electronic device. A specific implementation of the method includes: obtaining a multi-view video, where the multi-view video includes multi-view images, which include a video frame at an initial moment in the multi-view video; generating a three-dimensional scene model corresponding to the multi-view images; determining a deformable network corresponding to the multi-view video based on the three-dimensional scene model, the multi-view video, and camera pose information corresponding to the multi-view video; and determining a four-dimensional scene model corresponding to the multi-view video based on the three-dimensional scene model and the deformable network.