3D Voxel Delta Encoding Using XOR for Fast Compression
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Solution Overview
Problem
The large uncompressed data size of voxel space models for 3D objects makes them impractical for processing, especially when color and properties are encoded, requiring high-speed compression techniques to reduce data storage needs.
Innovation Solution
A high-speed compression method that voxelizes 3D models, applies XOR operations to identify changes between rows, uses run-length encoding, and further compresses or encrypts delta rows, allowing for parallel processing and efficient data reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If uncompressed voxel data is used to represent 3D models, then complete information is preserved, but data size becomes impractically large
Solution Approach 1:
The patent extracts only the essential information needed for 3D model representation by identifying and storing only changed voxel values between reference and target models, rather than storing complete uncompressed data. This extraction approach reduces data quantity while preserving necessary information for accurate model representation.
Solution Approach 2:
The patent segments voxel data into discrete comparable units and processes them through XOR operations to identify changes. By dividing the data into individual voxel elements that can be independently compared and encoded, the system achieves compression while maintaining information integrity for reconstruction.
2Quantity of substance
If traditional compression techniques are used on voxel data, then data size is reduced, but processing speed decreases
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with bitwise XOR operations that leverage CPU instruction-level parallelism. This substitution of computational approach enables simultaneous processing of multiple voxel data elements, dramatically increasing processing speed while achieving compression through bitwise operations rather than traditional compression mechanics.
Solution Approach 2:
The patent changes the computational parameters by using bitwise operations with fixed bit-width processing (e.g., 64-bit or 128-bit parallel XOR operations). This parameter change allows the system to process large blocks of voxel data in single computational steps, maintaining high processing speed while reducing data size through efficient bit-level manipulation.
3Productivity
If XOR operations are applied to compress voxel data, then compression speed increases, but data complexity increases
Solution Approach 1:
The patent performs preliminary organization of voxel data into structured arrays before applying XOR operations. By pre-arranging data in reference and target voxel arrays with consistent indexing, the system simplifies the subsequent compression process and makes the XOR operations more straightforward, reducing the effective complexity despite the use of bitwise operations.
Data Source
AI summary
One example of a method includes receiving a three-dimensional (3D) model of an object to be 3D printed and voxelizing the 3D model to define a plurality of voxel layers. Each voxel layer defines a plurality of voxel rows including an initial voxel row. The method includes for each voxel layer: exclusively oring (XORing) a key voxel row with the Initial voxel row to provide an initial delta row; and for each voxel row from the voxel row following the initial voxel row to a further voxel row of the plurality of voxel rows, XORing each voxel row with the previous voxel row to provide a delta row for each voxel row.