3D Primitive Fitting via Cascaded Global-Local Networks
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Solution Overview
Problem
Existing techniques face challenges in accurately fitting 3D primitives to high-resolution point clouds, particularly in detecting both large-scale and small-scale primitives with fine detail, due to limitations in memory handling and noise sensitivity, leading to sub-optimal fits and loss of fine detail.
Innovation Solution
A cascaded primitive fitting network is employed, comprising a global primitive fitting network for evaluating the entire point cloud and a local primitive fitting network for local patches, merging representations to compute multi-scale fitted primitives, which includes training on downsampled versions and utilizing contextual information to enhance local primitive fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If a single global primitive fitting network is used to evaluate the entire point cloud, then the global structure can be captured, but fine local details are lost
Solution Approach 1:
The point cloud is divided into multiple local patches that are processed separately by the local primitive fitting network. This segmentation allows the system to capture fine local details in each patch while the global network maintains overall structure awareness, resolving the contradiction between global shape representation and local detail precision.
Solution Approach 2:
The system operates at two different spatial scales simultaneously - global and local - adding a dimensional aspect of multi-scale analysis. This allows the global network to handle coarse structures while the local network captures fine details, effectively resolving the precision contradiction through dimensional expansion.
2Measurement precision
If high-resolution point clouds are processed directly, then fine detail can be detected, but memory requirements and computational complexity increase
Solution Approach 1:
By segmenting the high-resolution point cloud into smaller local patches, the system processes manageable portions separately. This reduces the memory footprint and computational complexity compared to processing the entire high-resolution cloud at once, while still preserving fine detail through localized analysis.
Solution Approach 2:
The system applies primitive fitting selectively to local patches rather than uniformly across the entire point cloud. This partial action approach focuses computational resources on regions of interest, reducing overall complexity while maintaining precision where needed.
3Device complexity
If traditional primitive fitting methods are used, then simplicity is maintained, but fitting accuracy on high-resolution data is sub-optimal
Solution Approach 1:
The dual-network architecture segments the fitting task into global and local components, each optimized for their respective scales. This segmentation enables higher fitting accuracy on high-resolution data by capturing both macro and micro geometric features, while maintaining reasonable system complexity through modular design.
Solution Approach 2:
The system uses a composite approach combining two different network architectures (global and local primitive fitting networks) that operate at different scales. This composite structure achieves superior fitting accuracy by integrating the strengths of both global context understanding and local detail capture.
Data Source
AI summary
Embodiments provide systems, methods, and computer storage media for fitting 3D primitives to a 3D point cloud. In an example embodiment, 3D primitives are fit to a 3D point cloud using a global primitive fitting network that evaluates the entire 3D point cloud and a local primitive fitting network that evaluates local patches of the 3D point cloud. The global primitive fitting network regresses a representation of larger (global) primitives that fit the global structure. To identify smaller 3D primitives for regions with fine detail, local patches are constructed by sampling from a pool of points likely to contain fine detail, and the local primitive fitting network regresses a representation of smaller (local) primitives that fit the local structure of each of the local patches. The global and local primitives are merged into a combined, multi-scale set of fitted primitives, and representative primitive parameters are computed for each fitted primitive.