Primitive-Based 3D Building Modeling from Sensor Data
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Solution Overview
Problem
Reconstructing realistic 3D building models from remote sensor data is challenging due to the complexity of building shapes and the need for compact representations, as conventional methods like polygonal mesh models require large amounts of costly annotated training data and struggle with noise and instability.
Innovation Solution
A system and method that utilize a 3D building modeling module to decompose buildings into geometric primitives, leveraging synthetic data for training and applying instance/same time segmentation and primitive fitting to generate a compact representation of buildings, which includes building simulation, decomposition, and 3D fitting processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If polygonal mesh models are used to represent buildings, then the representation can capture complex building shapes, but the model complexity and data requirements increase significantly
Solution Approach 1:
The patent segments building representations into two categories: simple buildings represented by primitive models (boxes, cylinders, cones) and complex buildings represented by polygonal mesh models. This segmentation allows the system to use simplified representations where applicable, reducing overall model complexity while maintaining accuracy for buildings that require it.
Solution Approach 2:
The patent changes the representation parameters from dense polygonal meshes to parametric primitive models with fewer parameters. Primitive models define buildings using simple geometric parameters (dimensions, orientation, position) rather than thousands of mesh vertices and faces, significantly reducing data requirements and computational complexity.
2Measurement precision
If conventional building modeling methods are used, then comprehensive building representations can be achieved, but large amounts of costly annotated training data are required
Solution Approach 1:
The patent uses synthetic data generation to create training examples by copying and transforming primitive building models into various configurations. Instead of requiring extensive real-world annotated data, the system generates synthetic training data by systematically varying parameters of known primitive models,大幅 reducing the need for costly manual annotation.
Solution Approach 2:
The system performs self-supervised learning where the synthetic data generation process automatically provides both training inputs and ground truth labels. The primitive models generate their own training data and annotations through parameter variation, eliminating the need for external annotation resources.
3Device complexity
If primitive based representation is used, then compact and regularized representation is achieved, but handling noise and instability in reconstruction remains challenging
Solution Approach 1:
The patent implements a dynamic hybrid representation system that can switch between primitive models and polygonal mesh models based on building complexity. This dynamic adaptation allows the system to maintain reconstruction stability by using robust primitive models for simple buildings while transitioning to flexible mesh models for complex structures where primitives would be insufficient.
Solution Approach 2:
The patent introduces an intermediary classification process that evaluates building complexity and determines the appropriate representation type. This intermediary step acts as a mediator between the simplicity requirements of primitive models and the accuracy requirements of complex building reconstruction, routing each building to the most suitable model type.
Data Source
AI summary
According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.