Usage Examples

February 5, 2026 · View on GitHub

Comprehensive examples for using the Depth Anything 3 ROS2 Wrapper with different cameras and configurations.

Quick Reference

Example	Use Case	Command
USB Camera	Generic webcam	`ros2 launch depth_anything_3_ros2 usb_camera_example.launch.py`
Static Image	Testing without camera	`ros2 launch depth_anything_3_ros2 image_publisher_test.launch.py`
ZED Camera	Stereo camera	See ZED Camera
RealSense	Intel depth camera	See RealSense
Multi-Camera	Multiple cameras	See Multi-Camera

Example 1: Generic USB Camera (v4l2_camera)

Complete example with a standard USB webcam:

# Install v4l2_camera if not already installed
sudo apt install ros-humble-v4l2-camera

# Option A: Use the provided launch file
ros2 launch depth_anything_3_ros2 usb_camera_example.launch.py \
  video_device:=/dev/video0 \
  model_name:=depth-anything/DA3-BASE

# Option B: Launch components separately
# Terminal 1: Camera driver
ros2 run v4l2_camera v4l2_camera_node --ros-args \
  -p image_size:="[640,480]" \
  -r __ns:=/camera

# Terminal 2: Depth estimation
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  image_topic:=/camera/image_raw \
  model_name:=depth-anything/DA3-BASE \
  device:=cuda

# Terminal 3: Visualization
rviz2 -d $(ros2 pkg prefix depth_anything_3_ros2)/share/depth_anything_3_ros2/rviz/depth_view.rviz

Example 2: ZED Stereo Camera

Connect to a ZED camera (requires separate ZED ROS2 wrapper installation):

# Launch ZED camera separately
ros2 launch zed_wrapper zed_camera.launch.py camera_model:=zedxm

# In another terminal, launch depth estimation with topic remapping
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  image_topic:=/zed/zed_node/rgb/image_rect_color \
  camera_info_topic:=/zed/zed_node/rgb/camera_info

# Or use the provided example
ros2 launch depth_anything_3_ros2 zed_camera_example.launch.py \
  camera_model:=zedxm

Example 3: Intel RealSense Camera

Connect to a RealSense camera (requires realsense-ros):

# Install RealSense ROS2 wrapper
sudo apt install ros-humble-realsense2-camera

# Launch RealSense camera
ros2 launch realsense2_camera rs_launch.py

# Launch depth estimation
ros2 launch depth_anything_3_ros2 realsense_example.launch.py

Example 4: Multi-Camera Setup

Run depth estimation on multiple cameras simultaneously:

# Launch multi-camera setup (4 cameras)
ros2 launch depth_anything_3_ros2 multi_camera.launch.py \
  camera_namespaces:="cam1,cam2,cam3,cam4" \
  image_topics:="/cam1/image_raw,/cam2/image_raw,/cam3/image_raw,/cam4/image_raw" \
  model_name:=depth-anything/DA3-BASE

Each camera gets its own namespaced depth topics:

/cam1/depth_anything_3/depth
/cam2/depth_anything_3/depth
etc.

Example 5: Testing with Static Images

Test with a static image using image_publisher (no camera required):

# Install image_publisher
sudo apt install ros-humble-image-publisher

# Launch with test image
ros2 launch depth_anything_3_ros2 image_publisher_test.launch.py \
  image_path:=/path/to/test_image.jpg \
  model_name:=depth-anything/DA3-BASE

Example 6: Different Models

Switch between models for different performance/accuracy tradeoffs:

# Fast inference (DA3-Small) - Best for real-time robotics
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  model_name:=depth-anything/DA3-SMALL \
  image_topic:=/camera/image_raw

# Balanced (DA3-Base) - Good accuracy and speed
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  model_name:=depth-anything/DA3-BASE \
  image_topic:=/camera/image_raw

# Best accuracy (DA3-Large) - Requires more GPU memory
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  model_name:=depth-anything/DA3-LARGE \
  image_topic:=/camera/image_raw

Example 7: CPU-Only Mode

For development or testing on systems without CUDA. Not recommended for production - use TensorRT on Jetson for real-time performance:

ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  image_topic:=/camera/image_raw \
  model_name:=depth-anything/DA3-BASE \
  device:=cpu

Note: CPU mode runs at ~1-2 FPS. For production deployment, use the TensorRT host-container architecture via ./run.sh.

Example 8: Custom Configuration

Use a custom parameter file for complex setups:

# Create custom config file
cat > my_config.yaml <<EOF
depth_anything_3:
  ros__parameters:
    model_name: "depth-anything/DA3-LARGE"
    device: "cuda"
    normalize_depth: true
    publish_colored: true
    colormap: "viridis"
    log_inference_time: true
    inference_height: 518
    inference_width: 518
EOF

# Launch with custom config
ros2 run depth_anything_3_ros2 depth_anything_3_node --ros-args \
  --params-file my_config.yaml \
  -r ~/image_raw:=/camera/image_raw

Jetson TensorRT Demo

For Jetson users, the one-click demo handles everything:

cd ~/depth_anything_3_ros2
./run.sh                           # Auto-detect camera
./run.sh --camera /dev/video0      # Specify camera
./run.sh --no-display              # Headless mode (SSH)

See Jetson Deployment Guide for details.

Advanced: Batch Processing ROS2 Bags

Process recorded ROS2 bags through depth estimation:

./scripts/ros2_batch_process.sh \
  -i ./raw_bags \
  -o ./depth_bags \
  -m depth-anything/DA3-BASE \
  -d cuda

Advanced: Performance Profiling

Profile ROS2 node performance:

python3 examples/scripts/profile_node.py \
  --model depth-anything/DA3-BASE \
  --device cuda \
  --duration 60

Topic Reference

Subscribed Topics

Topic	Type	Description
`~/image_raw`	sensor_msgs/Image	Input RGB image from camera
`~/camera_info`	sensor_msgs/CameraInfo	Optional camera intrinsics

Published Topics

Topic	Type	Description
`~/depth`	sensor_msgs/Image	Depth map (32FC1 encoding)
`~/depth_colored`	sensor_msgs/Image	Colorized depth visualization (BGR8)
`~/confidence`	sensor_msgs/Image	Confidence map (32FC1)
`~/depth/camera_info`	sensor_msgs/CameraInfo	Camera info for depth image

Next Steps

Configuration Reference - All parameters explained
ROS2 Node Reference - Node lifecycle, Jetson tuning
Optimization Guide - Performance tuning
Troubleshooting - Common issues