Point Clouds to IFC/BrIM Objective:
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1 Point Clouds to IFC/BrIM Objective: Develop and demonstrate a point cloud data processing solution, which takes a point cloud of a bridge obtained from laser scanning as input, and generates a solid model estimate of the bridge structure with semantic labels for its constitutive components as output.
2 Point Clouds to IFC/BrIM Scope: - No pre-existing BIM/IFC model. - High-level information regarding bridge available (from user). - Bridge categories: Girder and slab.
3 Point Clouds to IFC/BrIM Input: - Point cloud. - Minimum viable density (about 1 pt/cm). - Some user guidance (< 30 minutes). Sufficient Insufficient
4 Point Clouds to IFC/BrIM Output: - Industry Foundation Class (IFC) formatted model file. - Output level of development (LOD) around [1] Expectation: First pass 'best-guess' classifications, tagging the objects as - columns, - girders, - slabs, - etc. where possible. [2] [1] Structure Magazine [2] Mundo BIM
5 Point Clouds to IFC/BrIM What are strategies for going from dense or fine grain collections of raw data to its re-organization into components associated to a bigger entity? e.g., from points to bridge model?
6 Point Clouds to IFC/BrIM Bottom-Up Processing model (e.g. bridge) assemblies components surfaces points
7 Point Clouds to IFC/BrIM Top-Down Processing bridge assemblies clusters components
8 Point Clouds to IFC/BrIM Both approaches were pursued: 1. Top-Down 2. Bottom-Up with Top-Down partitioning
9 Point Clouds to IFC/BrIM Both approaches were pursued: 1. Top-Down 2. Bottom-Up with Top-Down partitioning
10 Top-down 3D geometry Goal : objective BrIM generation from PCD 2 major tasks Object detection in PCD Point clusters fitting Point cloud segmentation Point cluster classification
11 Top-down 3D geometry Top-down procedure A heuristic approach to the problem of object detection and object fitting Begins with a broad-picture view Explicitly incorporates engineering criteria as segmentation heuristics. General idea is to use the bridge topological and physical constraints. pier cap deck Knowledge Criteria pier pier cap pier pier cap algorithm pier
12 Top-down 3D geometry Partition span-wise to illuminate structure.
13 Top-down 3D geometry Refining and sub-dividing segmented components Pre-processing Step 1 Slicing X Step 2 Slicing Y Step 3 Refine pier area(s) slicing k-nn de-noising ConvexHull mesh generation OBB histogram Merging Step 5 Girder detection Step 4 Pier cap detection
14 Top-down 3D geometry Refining and sub-dividing segmented components START Pre-processing Step 1 Slicing X Step 2 Slicing Y Step 3 Refine pier area(s) slicing k-nn de-noising ConvexHull mesh generation OBB histogram Merging Step 5 Girder detection Step 4 Pier cap detection
15 Top-down 3D geometry Refining and sub-dividing segmented components START Pre-processing Step 1 Slicing X Step 2 Slicing Y Step 3 Refine pier area(s) Piers slicing k-nn de-noising ConvexHull mesh generation OBB histogram Merging Step 5 Girder detection Step 4 Pier cap detection
16 Top-down 3D geometry Refining and sub-dividing segmented components START Pre-processing Step 1 Slicing X Step 2 Slicing Y Step 3 Refine pier area(s) slicing k-nn de-noising ConvexHull mesh generation OBB histogram Merging Step 5 Girder detection Step 4 Pier cap detection Pier caps
17 Top-down 3D geometry Refining and sub-dividing segmented components START Pre-processing Step 1 Slicing X Step 2 Slicing Y Step 3 Refine pier area(s) END slicing k-nn de-noising ConvexHull mesh generation OBB histogram Merging Step 5 Girder detection Step 4 Pier cap detection Girders
18 Top-down 3D geometry Refining and sub-dividing segmented components
19 Top-down 3D geometry Ground Truth Preparation: Bridge 1 Bridge 2 Bridge 3 Bridge 4 Bridge 5 PCD BrIM Scanning(h) Segmentation(h) Modelling(h) Bridge 6 Bridge 7 Bridge 8 Bridge 9 Bridge 10 PCD BrIM Scanning(h) Segmentation(h) Modelling(h) average time (h) per bridge Scanning Segmentation Modelling
20 Top-down 3D geometry Experiments Data Preparation Manual segmentation Manual labelling
21 Top-down 3D geometry Results Point-level Component-level Precision Recall F1-score Avg 99.9% 99.9% 100.0% 94.3% 100.0% 100.0% 100.0% 99.1% 99.8% 99.0% 99.4% 87.8% 99.5% 99.8% 99.1% 98.1% 99.8% 99.5% 99.7% 91.0% 99.7% 99.9% 99.5% 98.6% Processing time min Manual 91.2 min ~70% time saving
22 Point Clouds to IFC/BrIM Both approaches were pursued: 1. Top-Down 2. Bottom-Up with Top-Down partitioning
23 Bottom-Up modeling Point cloud A bottom-up approach from top-down partitions. Surface primitive estimation Feature extraction Detect & classify components Reconstitute solid model geometry Package into IFC model file Top-down explicit User guidance Top-down implicit 1. Surface primitive and parametric model extraction 2. Detection and classification of bridge components from primitives proto- BIM 3. Bridge component parser for generating IFC model files.
24 Bottom-Up modeling Point cloud A bottom-up approach from top-down partitions. Surface primitive estimation Feature extraction Detect & classify components Reconstitute solid model geometry Package into IFC model file Top-down explicit User guidance Top-down implicit 1. Surface primitive and parametric model extraction 2. Detection and classification of bridge components from primitives proto- BIM 3. Bridge component parser for generating IFC model files.
25 Bottom-Up modeling Point cloud Clean/Partition Point Cloud Quadratic Surface Primitive Segmentation Model Estimation Get user After input cleaning, to Done for top-down point computational purposes. cloud and process define Main partitions bridge algorithm bridge. is O(n 2 ). frame minutes Model Merging Partition Merging Surface Models
26 Bottom-Up modeling Point cloud Clean/Partition Point Cloud Quadratic Surface Primitive Segmentation Uses information theory. Segmentation cost: coding length. Model Estimation Model Merging Partition Merging Surface Models Input points only few big surfaces many smaller ones Automated algorithm hours
27 Bottom-Up modeling Point cloud Clean/Partition Point Cloud Surface classification plus parametric fit. Quadratic Surface Primitive Segmentation plane? cylinder? girder points Model Estimation Model Merging Partition Merging Surface Models few big surfaces many smaller ones few big surfaces some medium ones Automated algorithm hours
28 Bottom-Up modeling Point cloud Clean/Partition Point Cloud Quadratic Surface Primitive Segmentation Model Estimation girder points Correct over-segmentation. Model Merging Partition Merging Surface Models User feedback. few big surfaces some medium ones Output only important surfaces 5-10 minutes
29 Bottom-Up modeling Point cloud Clean/Partition Point Cloud Quadratic Surface Primitive Segmentation Model Estimation Undo top-down partitioning. Model Merging Partition Merging Surface Models Piers + Superstructure (minus (with girders) + + Abutments + Soffits + Deck + Road
30 Bottom-Up modeling Point cloud A bottom-up approach from top-down partitions. Surface primitive estimation Feature extraction Detect & classify components Reconstitute solid model geometry Package into IFC model file Top-down explicit User guidance 1. Surface primitive and parametric model extraction Top-down implicit 2. Detection and classification of bridge components from primitives proto- BIM 3. Bridge component parser for generating IFC model files.
31 Bottom-Up modeling Classifier to output both hypothesized CAD model labels and hypothesize bridge components labels. Labels CAD Labels Component Labels Cuboid Cylinder Sheet Pier column Pier cap Girder Abutment Purpose: Reverse engineer the top-down process. Training requires top-level structure and lower-level equivalent. Implementation takes lower-level information to hypothesize top-level structure.
32 Bottom-Up modeling Classifier will output both hypothesized CAD model labels and hypothesize bridge components labels. Synthesize training data Train a multi-class classifier (boosted decision tree) Training phase [12 hours] Point cloud Classify and label each surface primitive CAD and Component Labels Surface Primitive Estimation Generate feature vectors for each surface primitive Implementation phase 5-10 minutes
33 Bottom-Up modeling Feature vector description of each surface element.
34 Bottom-Up modeling Sample output of bridge component classification.
35 Bottom-Up modeling Point cloud A bottom-up approach from top-down partitions. Surface primitive estimation Feature extraction Detect & classify components Reconstitute solid model geometry Package into IFC model file Top-down explicit User guidance 1. Surface primitive and parametric model extraction Top-down implicit 2. Detection and classification of bridge components from primitives proto- BIM 3. Bridge component parser for generating IFC model files.
36 Bottom-Up modeling Surface Model IFC Model
37 Bottom-Up modeling Surface Model IFC Model
38 Top-down 3D geometry Remaining work - Fitting
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