4D Dynamic Scene Reconstruction With Volumetric Error Correction
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Solution Overview
Problem
Conventional 3D volumetric modeling methods are inadequate for real-time high-quality modeling of dynamic objects, especially those with rapid movements, due to excessive volumetric prediction errors and high costs, making widespread adoption of mixed-reality applications challenging.
Innovation Solution
A system and method for real-time 4D dynamic scene interpretation, segmentation, and streaming using scalable GPUs and computer servers, with a novel error correction logic to address excessive volumetric prediction errors during rapid topology changes, enabling unsupervised and autonomous 3D human object model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional 3D volumetric modeling methods are used, then high-quality 3D modeling can be achieved, but the process is too expensive and time-consuming for widespread adoption
Solution Approach 1:
The patent segments the 3D volumetric modeling process into multiple independent stages: depth map generation, geometric model construction, texture mapping, and volumetric rendering. Each stage can be processed independently using different computational resources and algorithms, enabling optimization of quality without linearly increasing total processing time and cost.
Solution Approach 2:
The patent performs preliminary depth map extraction and geometric structure analysis in early processing stages, establishing the foundational 3D framework before applying detailed texture mapping and volumetric rendering. This preliminary action reduces the computational burden in later stages and accelerates overall processing while maintaining high modeling quality.
2Measurement precision
If conventional 3D volumetric modeling methods are used, then accurate 3D models can be generated, but time delays in processing prevent real-time applications
Solution Approach 1:
The patent implements a dynamic processing pipeline that adapts the level of detail and computational complexity based on real-time requirements. The system can switch between high-precision volumetric rendering and optimized geometric rendering modes, allowing real-time 3D model generation for dynamic scenes while maintaining accuracy where needed.
Solution Approach 2:
The patent transforms the processing approach by moving from traditional single-pass high-resolution rendering to a multi-pass dimensional decomposition, separating depth, geometry, and texture processing into distinct dimensional layers that can be processed in parallel, significantly reducing processing delay while maintaining model accuracy.
3Manufacturing precision
If specialized 3D image capture equipment is used, then high-quality 3D volumetric video contents can be created, but the equipment cost and complexity increase significantly
Solution Approach 1:
The patent uses standard 2D camera images as copies or representations of the 3D scene, extracting depth and geometric information through computational algorithms rather than requiring direct 3D capture hardware. This copying approach maintains high 3D volumetric content quality while eliminating the need for expensive specialized capture equipment.
Solution Approach 2:
The patent replaces mechanical 3D capture systems with computational geometry algorithms that process standard 2D images to generate 3D volumetric representations. This substitution eliminates complex mechanical depth sensors and multi-camera arrays, reducing equipment complexity while maintaining or improving 3D content quality through software-based depth estimation and mesh generation.
4Productivity
If conventional modeling methods are used for dynamic objects with rapid movements, then 3D models can be generated, but volumetric prediction errors increase excessively
Solution Approach 1:
The patent incorporates feedback mechanisms that continuously compare generated volumetric predictions with actual observed data from video frames. When prediction errors exceed thresholds, the system automatically adjusts its depth estimation and geometric reconstruction parameters, correcting volumetric inaccuracies in real-time during the processing of rapid movements.
Solution Approach 2:
The patent dynamically changes processing parameters such as depth map resolution, geometric refinement level, and volumetric sampling density based on the motion speed and complexity detected in each video frame. For rapid movements, the system adjusts parameters to prioritize speed while maintaining acceptable accuracy, preventing excessive prediction errors through adaptive parameter modulation.
Data Source
AI summary
A novel electronic system and its method of operation provide unsupervised and autonomous machine interpretation, segmentation, reconstruction, and streaming of three-dimensional (3D) dynamic scene and objects that contain various movements over a defined period. The elapsed time during the various movements of 3D scenes and objects acts as the fourth dimension, thus representing “4D” contents. The novel electronic system and its method of operation enable real-time and multiple dynamic 3D human object model generation with a novel volumetric prediction error correction logic, which uniquely mitigates frequent volumetric prediction errors that typically occur during computer graphical 3D modeling of fast-moving humans, who are undergoing video or image capture with a recording equipment. The volumetric prediction error correction logic is designed to intelligently enhance the accuracy and the quality of 3D modeling by correcting excessive volumetric prediction errors that often occur during static model generation due to rapid topology changes.