Multi-resolution voxel meshing for 3D reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D reconstruction techniques using fixed-size voxels fail to accurately represent physical environments due to insufficient detail with larger voxels and excessive resource usage with smaller voxels, leading to inaccurate or inefficient reconstructions.
Innovation Solution
The use of multi-resolution voxels, where voxel size and resolution are dynamically adjusted based on distance from the sensor and noise levels, allowing for finer resolution in areas with less noise and coarser resolution where noise is higher, stored in multiple hash tables to generate a mesh representing the physical environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If larger voxels are used, then resource usage is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The 3D space is segmented into multiple resolution levels, where different regions are divided into voxels of different sizes. Coarse voxels are used in regions with high noise or far from the sensor, while fine voxels are used in regions with low noise or close to the sensor. This segmentation allows the system to reduce the total number of voxels while maintaining precision where it matters most.
Solution Approach 2:
The patent applies local quality by assigning different voxel resolutions to different spatial regions based on their specific characteristics. Regions closer to the sensor or with lower noise levels receive finer voxel resolution, while regions farther away or with higher noise receive coarser resolution. This ensures that computational resources are allocated efficiently, focusing detail where it is most beneficial.
2Manufacturing precision
If smaller voxels are used, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic resolution adjustment where the voxel size is not fixed but adapts based on the local characteristics of the environment. The system dynamically determines which regions require fine resolution and which can use coarse resolution, allowing the reconstruction process to be more efficient while maintaining accuracy where needed. This dynamic approach reduces overall processing complexity compared to using uniformly fine voxels throughout.
Solution Approach 2:
The patent changes the parameter of voxel size based on spatial location and noise characteristics. By varying this fundamental parameter across different regions, the system achieves high precision in critical areas while reducing computational burden in less critical areas, thereby managing device complexity effectively.
3Manufacturing precision
If smaller voxels are used, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
By segmenting the 3D space into multiple resolution levels, the patent reduces the total number of voxels that need to be processed. Instead of uniformly applying fine voxels throughout the entire space, the system applies fine voxels only where necessary and coarse voxels elsewhere, significantly reducing processing time while maintaining reconstruction accuracy in critical regions.
Solution Approach 2:
The patent applies local quality by concentrating computational effort on regions where high precision is most beneficial, such as areas close to the sensor or regions with low noise. This localized approach to precision maintains reconstruction quality where it matters most while improving overall productivity by reducing the computational burden in other regions.
Data Source
AI summary
Various implementations disclosed herein generate a mesh representing the surfaces in a physical environment. The mesh is generated using multi-resolution voxels based on detected depth information, e.g., from a depth camera. The techniques may use multiple hash tables to store the multi-resolution voxel data. For example, the hash tables may store each voxel's 3D position and a truncated signed distance field (TSDF) value corresponding to each voxels' distance to a nearest surface. Each of the multiple hash tables may include data corresponding to a different level of resolution and those resolutions may depend upon distance/noise or other factors. For example, voxels close to a depth camera may have a finer resolution and smaller size compared to voxels that are further from the depth camera. Techniques disclosed herein may involve using a meshing algorithm that combines multi-resolution voxel information stored in multiple hash tables to generate a single mesh.