3D Model Compression via Instance Transformation Segmentation
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Solution Overview
Problem
Current 3D model compression methods fail to effectively handle 3D model properties like normal, color, and texture coordinates, especially when instance transformations include reflection, leading to inefficient compression and decoding processes.
Innovation Solution
A method and apparatus for generating a compressed bitstream that separates and compresses instance transformations into reflection, rotation, translation, and scaling parts, with options for elementary and grouped instance data modes, and uses Cartesian or spherical representations for rotation, allowing for flexible compression and decoding strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current 3D model compression methods are used, then compression is achieved, but they fail to handle 3D model properties like normal, color, and texture coordinates when instance transformations include reflection
Solution Approach 1:
The instance transformation is segmented into four distinct parts: reflection part, rotation part, translation part, and scaling part. Each part is compressed separately with dedicated data fields, allowing the system to handle diverse 3D model properties (normal, color, texture coordinates) and transformation types effectively without compromise in compression effectiveness.
2Ease of manufacture
If instance transformations are compressed as a whole, then compression is simpler, but reflection transformations cannot be handled properly
Solution Approach 1:
The transformation compression is divided into separate data fields for reflection, rotation, translation, and scaling. This segmentation maintains relative simplicity through structured organization while simultaneously enabling proper handling of reflection transformations and other transformation types that would be impossible to handle with a monolithic approach.
Solution Approach 2:
The compression system dynamically adapts to different transformation types by providing separate data fields that can be selectively applied. This allows the system to handle reflection, rotation, translation, and scaling transformations appropriately based on the specific instance requirements, achieving both simplicity and versatility.
3Measurement precision
If more detailed transformation data is stored, then decoding accuracy improves, but bitstream size increases
Solution Approach 1:
By segmenting transformation data into distinct parts (reflection, rotation, translation, scaling), the system can apply appropriate precision to each component based on its requirements. This allows maintaining high decoding accuracy for critical components while optimizing bitstream size by not over-specifying precision for less critical components.
Solution Approach 2:
Different data fields are used for different transformation components, allowing local optimization of data precision. Critical transformation parameters can be stored with higher precision while less critical parameters use compact representations, achieving the balance between decoding accuracy and bitstream size.
4Adaptability or versatility
If separate compression modes are provided, then adaptability to different applications improves, but system complexity increases
Solution Approach 1:
The compression system provides two operational modes (elementary instance data mode and grouped instance data mode) that can be dynamically selected based on application requirements. This dynamic flexibility achieves high adaptability while managing complexity through a unified underlying data structure that supports both modes.
Solution Approach 2:
The same set of transformation data fields (reflection, rotation, translation, scaling) serves multiple purposes and supports both compression modes universally. This multi-functionality allows the system to adapt to different applications without requiring separate complex systems for each mode, thereby managing complexity while maintaining versatility.
Data Source
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AI summary
A method and apparatus for generating a bitstream representative of a 3D model, and a method and an apparatus for processing the same. A 3D model is modeled by using a using a 'pattern-instance' representation, wherein a pattern is a representative geometry of a repetitive structure, and the connected components belonging to the repetitive structure is call an instance of the corresponding pattern. After discovery of the repetitive structures and their transformations and properties, the present embodiments provide for generating a bitstream in either a first format or a second format. In the first format, the pattern ID and its associated transformation and property information are grouped together in the bitstream, and in the second format the pattern ID, transformation property and property information are grouped together according to information type.