3D Reconstruction from Image Sequences Using Spatio-Temporal Volumes
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Solution Overview
Problem
Existing methods for generating three-dimensional models from two-dimensional images face challenges such as the correspondence problem and limited ability to estimate depth for features without a component perpendicular to the motion direction, leading to incomplete three-dimensional reconstructions.
Innovation Solution
A method involving the formation of a spatio-temporal volume from two-dimensional images, decomposition into cells, and densification to enhance three-dimensional information extraction, using a camera system with controlled motion and image processing to detect straight segments and interpolate depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If EPI analysis is used to solve the correspondence problem, then the correspondence problem is reduced to straight line detection, but depth of features without a component perpendicular to the motion direction cannot be estimated
Solution Approach 1:
The patent transitions from analyzing two-dimensional EPI slices to constructing a three-dimensional spatio-temporal volume by stacking multiple EPIs. This dimensional extension allows depth estimation for features that were previously invisible to EPI analysis, as the volumetric representation captures temporal evolution of features across multiple slices, enabling reconstruction of complete 3D geometry including interior points.
2Difficulty of detecting and measuring
If segment detection is used in the case of uniform linear camera motion, then the correspondence problem is simplified, but only objects boundaries may be reconstructed
Solution Approach 1:
The patent performs preliminary decomposition of the spatio-temporal volume into homogeneous cells before densification. This preliminary structuring allows subsequent interpolation to fill interior regions systematically. By establishing the cellular framework in advance, the method enables recovery of interior points that would otherwise be inaccessible through boundary-only segment detection.
Solution Approach 2:
The patent replaces the mechanical segment detection approach (which only captures boundaries) with an interpolation-based densification process. This substitution uses computational geometry and mathematical interpolation to generate dense 3D point clouds from sparse boundary information, effectively replacing the limitation of mechanical detection with a computational solution that recovers interior structure.
3Difficulty of detecting and measuring
If camera motion restrictions are complied with for EPI formation, then straight line detection becomes possible, but complying with these restrictions may be difficult
Solution Approach 1:
The patent employs dynamic camera motion along a curved trajectory rather than requiring static uniform linear motion. The curved path allows the camera to capture features from multiple angular perspectives while maintaining the temporal coherence needed for volumetric reconstruction. This dynamic approach relaxes the rigid motion constraints of traditional EPI methods while preserving the ability to form meaningful spatio-temporal volumes for 3D reconstruction.
Data Source
AI summary
A method and apparatus to extract a dense three-dimensional model of an observed scene or object from one or more sequences of images acquired with an imaging device such as a camera or camcorder, or clusters of cameras and/or camcorders. In some embodiments the method includes capturing an image sequence by a camera moving with a translateral motion, for example moving on a straight path with a fixed orientation. The images in each sequence are captured at regularly spaced sites on the straight path. In some embodiments, a device projects structured light on the observed scene. Multiple image sequences can be acquired, on different cameras on straight paths. The captured image sequences are input to a computer. The 3D structure of the scene viewed by each image sequence is computed. Dense reconstruction from EPIs is performed by interpolating the EPIs after having detecting straight segments on the EPIs. 3D models extracted from each image sequence are combined and merged to create a 3D model of the whole scene.