Asset Inventory and Data Export#

Now that you’ve learned to navigate your factory scene visually using waypoints, let’s explore how to navigate it programmatically using Python scripting. This section teaches you to automatically traverse your USD stage, identify all assets, and export the data to CSV format for facility management and reporting purposes.

Understanding Stage Traversal#

Stage traversal is the process of systematically walking through your USD scene’s hierarchy to query or extract data from every primitive (prim) in the stage. Think of it as an automated way to “visit” every object in your digital twin and collect information about it.

Tip

For a deep dive into Stage traversal and more OpenUSD topics, check out our learning path.

Why Stage Traversal Matters for Digital Twins#

As your factory digital twin grows in complexity, manually tracking assets becomes impractical. Consider these scenarios:

Facility Management Needs

“How many safety devices do we have in the facility?”
“What types of machines are installed in Production Line A?”
“Which assets need maintenance based on their installation dates?”
“What’s the total count of each asset type for procurement planning?”

In real facility management, you need to:

Generate asset inventories for maintenance planning
Create compliance reports showing all safety equipment locations
Track component installations across production lines
Export data for ERP integration and facility management systems

Manual clicking through hundreds of objects isn’t practical—scripting automates this essential workflow.

How USD Stage Traversal Works#

USD traversal operates by walking through prims in depth-first order

/Factory (Root)
├── /Factory/ProductionLine_A (visited first)
│   ├── /Factory/ProductionLine_A/Machine_001 (visited second)
│   └── /Factory/ProductionLine_A/Machine_002 (visited third)
└── /Factory/ProductionLine_B (visited fourth)
    └── /Factory/ProductionLine_B/Machine_003 (visited fifth)

This systematic approach ensures every prim gets visited exactly once, in a predictable order.

Python Scripting in Omniverse#

Accessing the Script Editor

Navigate to Developer > Script Editor to open the Script Editor.

Basic Stage Traversal: Listing All Assets#

Let’s start with a fundamental script that traverses your factory stage and lists every prim path.

Script 1: Complete Stage Traversal#

Warning

Depending on the size of your scene this may take some time to complete.

Copy and paste this script into the Script Editor

# Import necessary USD modules
from pxr import Usd

# Get the current stage (your opened factory scene)
stage = omni.usd.get_context().get_stage()

# Check if stage is valid
if not stage:
    print("No stage currently loaded!")
else:
    print("=== Factory Scene Traversal ===")
    print("Traversing all prims in depth-first order:\n")
    
    # Traverse all active, loaded, defined prims
    prim_count = 0
    for prim in stage.Traverse():
        print(f"Path: {prim.GetPath()}")
        print(f"Type: {prim.GetTypeName()}")
        print(f"Kind: {prim.GetKind()}")
        print("-" * 40)
        prim_count += 1
    
    print(f"\nTotal prims traversed: {prim_count}")

Execute the script and observe:

The hierarchical structure of your factory scene
Prim types (Xform, Mesh, Material, etc.)
USD Kind metadata (Component, Assembly, Group)
Total count of all objects in your scene

Understanding the Output#

Your results will show the organized structure of your digital twin:

Output Element	What It Tells You	Why It Matters
Path	Hierarchical location in scene	Navigation and organization
Type	USD primitive type (Mesh, Xform, etc.)	Technical structure understanding
Kind	Asset classification (Component, Assembly)	Logical grouping and filtering

Targeted Asset Identification#

Rather than listing everything, let’s create more focused scripts that identify specific types of assets relevant to facility management.

Script 2: Finding Manufacturing Equipment#

This script identifies machines and equipment specifically

Note

This script finds the machine assets by searching for these keywords in the name or if the Kind is set to Component. Keywords include: machine,** robot**, conveyor, etc.

from pxr import Usd, UsdGeom

# Get current stage
stage = omni.usd.get_context().get_stage()

if stage:
    print("=== Manufacturing Equipment Inventory ===\n")
    
    machines = []
    
    for prim in stage.Traverse():
        # Look for prims with "machine" in the name or specific kinds
        prim_name = prim.GetName().lower()
        prim_kind = prim.GetKind()
        
        # Identify potential manufacturing equipment
        if (any(keyword in prim_name for keyword in ['machine', 'robot', 'conveyor', 'press', 'router', 'feeder']) 
            or prim_kind == 'Component'):
            
            # Get additional information
            transform = UsdGeom.Xformable(prim)
            
            machine_data = {
                'name': prim.GetName(),
                'path': str(prim.GetPath()),
                'type': prim.GetTypeName(),
                'kind': prim_kind
            }
            
            machines.append(machine_data)
            
            print(f"Equipment: {machine_data['name']}")
            print(f"Location: {machine_data['path']}")
            print(f"Type: {machine_data['type']}")
            print(f"Classification: {machine_data['kind']}")
            print("-" * 50)
    
    print(f"\nTotal manufacturing equipment found: {len(machines)}")

Script 3: Props and Safety Equipment Inventory#

This script focuses on props, safety equipment, and environmental objects

Note

This script is searching prims similar to the previous one, but it will categorize them by type.
Such as: Safety Equipment, Storage/Transport, Lighting

from pxr import Usd

# Get current stage
stage = omni.usd.get_context().get_stage()

if stage:
    print("=== Props and Safety Equipment Inventory ===\n")
    
    props = []
    safety_equipment = []
    
    for prim in stage.Traverse():
        prim_name = prim.GetName().lower()
        prim_path = str(prim.GetPath()).lower()
        
        # Categorize based on name patterns
        if any(keyword in prim_name for keyword in ['cone', 'barrier', 'sign', 'safety', 'warning']):
            safety_equipment.append({
                'name': prim.GetName(),
                'path': str(prim.GetPath()),
                'category': 'Safety Equipment'
            })
            
        elif any(keyword in prim_name for keyword in ['pallet', 'container', 'box', 'rack', 'shelf']):
            props.append({
                'name': prim.GetName(),
                'path': str(prim.GetPath()),
                'category': 'Storage/Transport'
            })
            
        elif any(keyword in prim_name for keyword in ['light', 'lamp', 'fixture']):
            props.append({
                'name': prim.GetName(),
                'path': str(prim.GetPath()),
                'category': 'Lighting'
            })
    
    # Display results
    print("SAFETY EQUIPMENT:")
    for item in safety_equipment:
        print(f"  {item['name']} at {item['path']}")
    
    print(f"\nPROPS BY CATEGORY:")
    for item in props:
        print(f"  {item['category']}: {item['name']} at {item['path']}")
    
    print(f"\nSummary:")
    print(f"Safety Equipment: {len(safety_equipment)}")
    print(f"Props: {len(props)}")

CSV Export: Asset Data Export#

Now let’s create a comprehensive script that exports all asset data to CSV format for external use.

This final script combines all the techniques we’ve learned to create a focused, efficient export. Unlike the earlier exploratory scripts, this version is optimized for performance and targets only assets with specific custom attributes that you’ve added to your scene.

Key Features of this Export Script

Targeted Filtering Only exports assets that contain your custom metadata attributes, making the process faster and the results more relevant for facility management.

Smart Categorization Automatically categorizes assets based on naming patterns, grouping them into meaningful categories like “Manufacturing Equipment,” “Safety Equipment,” and “Storage/Transport.”

Structured Output Creates a standardized CSV format that can be easily imported into facility management systems, maintenance databases, or spreadsheet applications.

Performance Optimization Uses efficient USD methods to check for specific attributes without processing every prim’s complete attribute list.

Note

To change the location that the file will save to, search for the comment in the code:

# Set your CSV file save location here
CSV_SAVE_PATH = "C:/Users/YourName/Desktop/factory_inventory.csv"

Understanding the TARGET_ATTRIBUTES Configuration

The script searches specifically for these custom attributes:

InstallationDate - When the asset was installed
FloorLevel - Physical floor location
AssetTag - Unique facility identifier
LocationCode - Facility location reference
ResponsibleDepartment - Owning department
SerialNumber - Manufacturer serial number

Only assets containing at least one of these attributes will be included in the export, ensuring your CSV contains meaningful facility management data rather than every geometric primitive in the scene.

Interpreting Your Export Results

After running the script, you’ll see console output showing:

Export confirmation with file location and asset count
Category breakdown showing how many assets were found in each type
File location where you can open the CSV in Excel or your preferred application

The resulting CSV file provides a complete asset inventory that bridges your 3D digital twin with traditional facility management workflows, enabling data-driven maintenance planning, compliance reporting, and operational decision-making.

from pxr import Usd
import csv
import os

# ===== CONFIGURATION =====
# Set your CSV file save location here
CSV_SAVE_PATH = "C:/Users/YourName/Desktop/factory_inventory.csv"

# Define the specific custom attributes to search for
TARGET_ATTRIBUTES = [
    'InstallationDate',
    'FloorLevel',
    'AssetTag', 
    'LocationCode',
    'ResponsibleDepartment',
    'SerialNumber'
]
# =========================

stage = omni.usd.get_context().get_stage()

if stage:
    print("Exporting factory inventory with attributes...")
    
    inventory_data = []
    
    for prim in stage.Traverse():
        # Check for target attributes
        found_attributes = {}
        for attr_name in TARGET_ATTRIBUTES:
            if prim.HasAttribute(attr_name):
                try:
                    value = prim.GetAttribute(attr_name).Get()
                    found_attributes[attr_name] = str(value) if value is not None else ''
                except:
                    found_attributes[attr_name] = 'Error reading value'
        
        # Only include prims with target attributes
        if found_attributes:
            # Categorize based on name patterns
            name_lower = prim.GetName().lower()
            if any(keyword in name_lower for keyword in ['machine', 'robot', 'press', 'router', 'feeder']):
                category = 'Manufacturing Equipment'
            elif any(keyword in name_lower for keyword in ['cone', 'barrier', 'safety', 'warning']):
                category = 'Safety Equipment'
            elif any(keyword in name_lower for keyword in ['pallet', 'container', 'box']):
                category = 'Storage/Transport'
            elif any(keyword in name_lower for keyword in ['light', 'lamp']):
                category = 'Lighting'
            else:
                category = 'Other'
            
            prim_data = {
                'Name': prim.GetName(),
                'Path': str(prim.GetPath()),
                'Type': prim.GetTypeName(),
                'Kind': prim.GetKind(),
                'Category': category
            }
            prim_data.update(found_attributes)
            inventory_data.append(prim_data)
    
    if inventory_data:
        # Write CSV
        base_columns = ['Name', 'Path', 'Type', 'Kind', 'Category']
        target_columns = [attr for attr in TARGET_ATTRIBUTES if any(attr in item for item in inventory_data)]
        columns = base_columns + target_columns
        
        with open(CSV_SAVE_PATH, 'w', newline='', encoding='utf-8') as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=columns)
            writer.writeheader()
            for item in inventory_data:
                row = {col: item.get(col, '') for col in columns}
                writer.writerow(row)
        
        print(f"✅ Exported {len(inventory_data)} assets to: {CSV_SAVE_PATH}")
        
        # Category summary
        categories = {}
        for item in inventory_data:
            cat = item['Category']
            categories[cat] = categories.get(cat, 0) + 1
        
        for category, count in sorted(categories.items()):
            print(f"   {category}: {count}")
            
    else:
        print("No assets found with target attributes.")
        
else:
    print("No USD stage loaded.")

Factory Inventory Export Results#

Name	Path	Type	Kind	Category	Installation Date	Asset Tag	Responsible Department	Serial Number
CL6_Line_Full	/World/CL6_Line_Full	Xform	assembly	Manufacturing Equipment	1/22/2024	FAC-FDR-003	Manufacturing	N03-FD-2024-0156
CL6_Line_OnBoard_Router	/World/CL6_Line_OnBoard_Router	Xform	assembly	Manufacturing Equipment	8/15/2025	FAC-RTR-001	Production Engineering	CL6-RT-2023-0847
CL6_Line_Small	/World/CL6_Line_Small	Xform	assembly	Manufacturing Equipment	3/10/2025	FAC-ASM-005	Assembly Operations	ASM-A-2024-0298
CL6_Line_Full_01	/World/CL6_Line_Full_01	Xform	assembly	Manufacturing Equipment	6/5/2025	FAC-QIR-007	Quality Assurance	QIR-07-2024-0445
WarehousePile_A1	/World/Props/WarehousePile_A1	Xform	component	Other	11/8/2023	FAC-SHF-201	Inventory Management
RackSmall_A7	/World/Props/RackSmall_A7	Xform	component	Other	11/8/2023	FAC-SHF-202	Inventory Management
PopUpCone_A04_71cm_PR_NVD_02	/World/Props/PopUpCone_A04_71cm_PR_NVD_02	Xform	component	Safety Equipment	11/8/2023	FAC-SC-012	Safety & Compliance	SC-ORG-2024-0891

Practical Applications and Next Steps#

Real-World Use Cases#

The CSV export you’ve created enables:

Use Case	Data Application	Business Value
Maintenance Planning	Asset locations and schedules	Preventive maintenance optimization
Compliance Reporting	Safety equipment inventory	Regulatory compliance documentation
Facility Management	Complete asset database	Integration with CMMS systems
Change Management	Before/after asset comparisons	Impact assessment and planning

Extending the Scripts#

You can enhance these scripts by:

Adding filters for specific production lines or areas
Including geometric data like bounding box dimensions
Collecting material information for procurement planning
Integrating with databases for real-time facility management
Generating reports in different formats (JSON, Excel, XML)

Integration with Facility Systems#

This scripting approach transforms your visual digital twin into a data-rich facility management tool, bridging the gap between 3D visualization and operational business systems. The automated inventory generation you’ve learned represents a fundamental capability for industrial digital twin applications.