3D Reconstruction with Directional Distance Functions
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Solution Overview
Problem
Reconstruction algorithms for 3D models face challenges such as incompleteness and inaccuracy due to occlusions and noise, especially when dealing with dynamic objects and non-rigid structures, which are not effectively addressed by existing methods.
Innovation Solution
The introduction of a Directional Distance Function (DDF) that incorporates a direction field and a signed distance field, integrated with energy minimization techniques using gradient descent methods, to efficiently align and track dynamic objects in 3D reconstruction, incorporating color consistency and dense point cloud alignment to handle noisy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reconstruction algorithms are used for dynamic objects, then the processing speed is maintained, but the completeness and accuracy of the 3D model deteriorate due to occlusions and noise
Solution Approach 1:
The patent applies preliminary action by pre-segmenting the scene into static and dynamic objects before reconstruction. Static objects are reconstructed first to establish a stable reference framework, which then serves as a foundation for tracking and reconstructuring dynamic objects. This preliminary segmentation and static object reconstruction prevents occlusion problems from compromising the overall model completeness.
Solution Approach 2:
The patent divides the reconstruction problem into separate segments: static object reconstruction and dynamic object tracking. By segmenting the scene and processing different object types with specialized algorithms, the system achieves both accuracy for tracked dynamic objects and completeness for the overall scene, resolving the contradiction between measurement precision and reliability.
2Measurement precision
If temporal information is used to improve reconstruction quality, then the accuracy improves for rigid structures, but the method fails to effectively address dynamic objects with non-rigid motion
Solution Approach 1:
The patent applies dynamics by implementing separate tracking mechanisms: rigid transformation for static objects and non-rigid deformation models for dynamic objects. The system adapts the temporal information processing method based on object type, using appearance-based tracking with deformation modeling for dynamic objects, thereby achieving both high reconstruction quality and adaptability to non-rigid motion.
Solution Approach 2:
The patent applies local quality by using different reconstruction and tracking methods tailored to specific object types. Static objects receive rigid transformation tracking while dynamic objects receive non-rigid deformation tracking with appearance-based matching. This localized approach ensures optimal reconstruction quality for each object type while maintaining overall system versatility.
3Device complexity
If depth camera data is used for 3D reconstruction, then the reconstruction process is simplified, but the data quality deteriorates due to noise and occlusions
Solution Approach 1:
The patent merges multiple data sources and processing stages: depth camera data is combined with appearance information from color cameras, and results from static object reconstruction are integrated with dynamic object tracking. This merging compensates for the weaknesses of individual sensors and methods, improving data quality while maintaining reasonable process complexity through unified processing frameworks.
Solution Approach 2:
The patent introduces intermediate processing steps including segmentation algorithms that separate static and dynamic objects, and appearance-based tracking that mediates between depth data and final 3D reconstruction. These intermediaries filter and refine noisy depth camera data, improving measurement precision while keeping the overall process manageable through modular architecture.
Data Source
AI summary
A video conference server receives a plurality of video frames including a current frame and at least one previous frame. Each of the video frames includes a corresponding image and a corresponding depth map. The server produces a directional distance function (DDF) field that represents an area surrounding a target surface of the object captured in the current frame. A forward transformation is generated that modifies the reference surface to align with the target surface. Using at least a portion of the forward transformation, a backward transformation is calculated that modifies the target surface of the current frame to align with the reference surface. The backward transformation is then applied to the DDF to generate a transformed DDF. The server updates the reference model with the transformed DDF and transmits data for the updated reference model to enable a representation of the object to be produced at a remote location.