3D Scene Reconstruction with Normal-Segmentation Regularization
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Solution Overview
Problem
Current 3D scene reconstruction methods face challenges with global and local artifacts such as distorted room shapes, holes, and hills due to imperfections in input data, particularly in indoor environments with planar surfaces, where minor errors in input data can lead to corrupted scene geometry.
Innovation Solution
The introduction of Normal-Segmentation Regularization (NSR), a trainable module that predicts TSDF and segmentation classes, penalizing deviations of surface normals from horizontal and vertical directions, integrated into existing 3D reconstruction pipelines to improve scene reconstruction accuracy and smoothness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional 3D reconstruction methods are used, then reconstruction speed is improved, but reconstruction quality deteriorates due to global and local artifacts
Solution Approach 1:
The patent segments the reconstruction process into multiple stages: initial fast reconstruction using traditional methods, followed by iterative refinement stages that address specific artifact types (global structure correction, local surface smoothing, hole filling). This segmentation allows the system to achieve both speed and quality by handling different reconstruction aspects in separate optimization passes.
Solution Approach 2:
The patent changes reconstruction parameters dynamically across different stages. Early stages use parameters optimized for speed (coarser resolution, fewer constraints), while later stages use parameters optimized for quality (fine-grained control, strong geometric constraints). The system adjusts resolution, iteration counts, and constraint strengths based on the current reconstruction state to balance speed and quality.
2Manufacturing precision
If input data completeness is improved, then reconstruction quality is improved, but data collection complexity increases
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images from different viewpoints before the actual reconstruction process. This pre-data collection phase ensures sufficient coverage of the scene from various angles, creating a robust foundation that reduces the need for complex real-time adjustments during reconstruction. The system prepares and organizes this preliminary data to streamline subsequent processing.
Solution Approach 2:
The patent implements feedback mechanisms that monitor reconstruction quality in real-time and adjust data collection requirements accordingly. When the system detects sufficient data quality for a particular region, it reduces further data collection efforts in that area. Conversely, when artifacts are detected, the system automatically triggers additional data capture or refinement operations in affected regions, optimizing the balance between data completeness and collection complexity.
3Productivity
If feature aggregation complexity is reduced, then processing speed is improved, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent segments feature aggregation into hierarchical levels: local feature aggregation for detailed accuracy, middle-range aggregation for structural consistency, and global aggregation for overall scene coherence. Each level processes features independently with appropriate complexity, allowing the system to maintain accuracy while improving processing speed by avoiding the computational burden of aggregating all features simultaneously.
Solution Approach 2:
The patent applies local quality principles by aggregating features with different levels of detail in different spatial regions. High-frequency detail features are aggregated locally for accurate surface representation, while low-frequency structural features are aggregated globally for pose estimation and scene structure. This localized approach reduces overall computational complexity while maintaining reconstruction accuracy through region-specific optimization.
Data Source
AI summary
A method for 3D scene reconstruction and visualization, may include, using at least one processor: obtaining a trained base neural network by training the base neural network for obtaining distance information for voxels of a real scene; operating the trained base neural network for obtaining the distance information of an input sequence of frames of the real scene; inputting the distance information to an algorithm that outputs a 3D reconstruction of the real scene; obtaining a 3D visualization of the real scene by rendering the 3D reconstruction of the real scene; and instructing at least one display to display the 3D visualization of the real scene.