Oriented-Grid Encoding for Detailed 3D Implicit Representation
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Solution Overview
Problem
Existing 3D implicit representation techniques fail to consider an object's underlying geometric characteristics, leading to unsatisfactory representations.
Innovation Solution
A neural network is trained with an encoder that encodes 3D point cloud data into grid-based features capturing multiple resolutions, and a decoder that decodes these features into a distance to the object from an arbitrary point, considering the object's orientations and geometric characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional encoders only use spatial localization, then the encoding process is simple, but the 3D representation quality is unsatisfactory
Solution Approach 1:
The encoder is divided into multiple specialized components: oriented-grid geometric encoder for spatial structure, cylindrical interpolation module for smooth transitions, and normal integration module for geometric characteristics. Each component handles a specific aspect of the encoding process, collectively achieving high-quality 3D representation while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent extends traditional 3D spatial encoding by incorporating orientation information as an additional dimension. The oriented-grid encoder processes both position and normal vector data, creating a multi-dimensional feature space that captures comprehensive geometric characteristics, thereby improving representation quality beyond simple spatial localization.
2Measurement precision
If grid-based encoders capture multiple resolutions, then the representation detail improves, but the computational complexity increases
Solution Approach 1:
The patent implements a nested multi-resolution grid structure where coarse grids provide overall scene context and finer grids capture local details. Each resolution level is nested within the previous level, allowing the system to capture detailed geometric features while using coarser levels to reduce computational burden through hierarchical processing.
Solution Approach 2:
The oriented-grid encoder applies different grid resolutions locally based on geometric complexity. Regions with high curvature or important features use finer grids for detailed representation, while flat or less important regions use coarser grids, optimizing the balance between representation detail and computational complexity through adaptive local refinement.
Data Source
AI summary
An artificial intelligence system for producing an implicit representation of a three-dimensional (3D) scene including a 3D object by training a neural network including an encoder configured for encoding data indicative of a 3D point cloud of a shape of the object into grid-based features capturing multiple resolutions of the object and a decoder, the system comprising: a processor and a memory having instructions stored thereon that cause the processor to: (i) receive input data indicative of an oriented point cloud of a 3D scene including a 3D object, the input data indicating 3D locations of points of the 3D point cloud and orientations of the points defining a normal to a surface of the 3D object at locations proximate to the 3D locations of the points; and (ii) train the encoder and the decoder to produce an implicit representation of the 3D object.