Plan View Wall Detection for Indoor Point Clouds Using Weak Supervision
Publication: Computing in Civil Engineering 2023
ABSTRACT
We present an automated scan-to-BIM pipeline that simplifies the 3D building object recognition problem into a 2D recognition problem. We used the Habitat Matterport 3D Dataset (HM3D) for training wall detection model. The weakly supervised learning is conducted since we used the noisy depth-projected annotation. We isolated individual building levels and projected the points to 2D along the Z-axis (up/down). The architectural components’ recognition system detects walls within the plan view projection of the indoor point cloud. We compare the performance metric of validation on noisy annotation with human-labeled annotation and analyze the wall inference results from visualization. We assume the human-labeled annotation as ground truth and noisy annotation is prediction to calculate the average precision. The average precision values are compared to the neural network performance. We anticipate this experiment can provide a feasible weak supervision method for simplifying 3D digital model creation from scan data.
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Published In
Computing in Civil Engineering 2023
Pages: 75 - 82
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Buildings
- Business management
- Computer networks
- Computer vision and image processing
- Computing in civil engineering
- Construction engineering
- Construction management
- Engineering fundamentals
- Human and behavioral factors
- Methodology (by type)
- Models (by type)
- Personnel management
- Practice and Profession
- Project management
- Structural engineering
- Structural members
- Structural systems
- Structures (by type)
- Three-dimensional models
- Walls
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