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Depth Anything 3 ROS2 Wrapper

Acknowledgments and Credits

This package would not be possible without the excellent work of the following projects and teams:

Depth Anything 3

Team: ByteDance Seed Team
Repository: ByteDance-Seed/Depth-Anything-3
Paper: Depth Anything 3: A New Foundation for Metric and Relative Depth Estimation
Project Page: https://depth-anything-3.github.io/

This wrapper integrates the state-of-the-art Depth Anything 3 model for monocular depth estimation. All credit for the model architecture and training goes to the original authors.

Inspiration from Prior ROS2 Wrappers

This package was inspired by the following excellent ROS2 wrapper implementations:

Depth Anything V2 ROS2: grupo-avispa/depth_anything_v2_ros2
Depth Anything ROS2: polatztrk/depth_anything_ros
TensorRT Optimized Wrapper: scepter914/DepthAnything-ROS

Special thanks to these developers for demonstrating effective patterns for ROS2 integration.

Overview

This aims to be a camera-agnostic ROS2 wrapper for Depth Anything 3 (DA3), providing real-time monocular depth estimation from standard RGB images. This package is designed to work seamlessly with any camera publishing standard sensor_msgs/Image messages.

Key Features

Camera-Agnostic Design: Works with ANY camera publishing standard ROS2 image topics
Multiple Model Support: All DA3 variants (Small, Base, Large, Giant, Nested)
CUDA Acceleration: Optimized for NVIDIA GPUs with automatic CPU fallback
Multi-Camera Support: Run multiple instances for multi-camera setups
Real-Time Performance: Optimized for low latency on Jetson Orin AGX
Production Ready: Comprehensive error handling, logging, and testing
Docker Support: Pre-configured Docker and Docker Compose files
Example Images: Sample test images and benchmark scripts included
Performance Profiling: Built-in benchmarking and profiling tools
TensorRT Support: Optimization scripts for NVIDIA Jetson platforms
Post-Processing: Depth map filtering, hole filling, and enhancement
INT8 Quantization: Model compression for faster inference
ONNX Export: Deploy to various platforms and runtimes
Complete Documentation: Sphinx-based API docs with comprehensive tutorials
CI/CD Ready: GitHub Actions workflow for automated testing and validation
Docker Testing: Automated Docker image validation suite
RViz2 Visualization: Pre-configured visualization setup

Supported Platforms

Primary: NVIDIA Jetson (JetPack 6.x)
Compatible: Any system with Ubuntu 22.04, ROS2 Humble, and CUDA 12.x
ROS2 Distribution: Humble Hawksbill
Python: 3.10+

Important: Dependencies and Model Downloads

You do NOT need to manually clone the ByteDance Depth Anything 3 repository. The installation process handles everything automatically.

What Gets Installed

1. Python Package (installed via pip in Step 2):

ByteDance DA3 Python API and inference code
Installed with: pip install git+https://github.com/ByteDance-Seed/Depth-Anything-3.git
Pip handles cloning and installation automatically
One-time setup, no manual git clone needed

2. Pre-Trained Models (downloaded automatically on first run):

Model weights download from Hugging Face Hub on first use
Cached in ~/.cache/huggingface/hub/ for reuse
Internet connection required for initial download
Subsequent runs use cached models (no internet needed)

Summary: Install the package once with pip (Step 2), then models download automatically when you first run the node.

Offline Operation (Robots Without Internet)

For robots or systems without internet access, pre-download models on a connected machine:

# On a machine WITH internet connection:
python3 -c "
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
# Download model (only needs to be done once)
AutoImageProcessor.from_pretrained('depth-anything/DA3-BASE')
AutoModelForDepthEstimation.from_pretrained('depth-anything/DA3-BASE')
print('Model downloaded to ~/.cache/huggingface/hub/')
"

# Copy the cache directory to your offline robot:
# On source machine:
tar -czf da3_models.tar.gz -C ~/.cache/huggingface .

# On target robot (via USB drive, SCP, etc.):
mkdir -p ~/.cache/huggingface
tar -xzf da3_models.tar.gz -C ~/.cache/huggingface/

Alternatively, set a custom cache directory:

# Download to specific location
export HF_HOME=/path/to/models
python3 -c "from transformers import AutoModelForDepthEstimation; \
            AutoModelForDepthEstimation.from_pretrained('depth-anything/DA3-BASE')"

# On robot, point to the same location
export HF_HOME=/path/to/models
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py

Available Models:

depth-anything/DA3-SMALL - Fastest, ~1.5GB download
depth-anything/DA3-BASE - Balanced, ~2.5GB download
depth-anything/DA3-LARGE - Best quality, ~4GB download
depth-anything/DA3-GIANT - Maximum quality, ~6.5GB download

Installation

Prerequisites

ROS2 Humble on Ubuntu 22.04:

# If not already installed
sudo apt update
sudo apt install ros-humble-desktop

CUDA 12.x (optional, for GPU acceleration):

# For Jetson Orin AGX, this comes with JetPack 6.x
# For desktop systems, install CUDA Toolkit from NVIDIA
nvidia-smi  # Verify CUDA installation

Internet Connection (for initial setup):

Required for Step 2 (pip install of DA3 package)
Required for Step 5 (model weights download from Hugging Face Hub)
See Offline Operation if deploying to robots without internet

Step 1: Install ROS2 Dependencies

sudo apt install -y \
  ros-humble-cv-bridge \
  ros-humble-sensor-msgs \
  ros-humble-std-msgs \
  ros-humble-image-transport \
  ros-humble-rclpy

Step 2: Install Python Dependencies

# Create and activate a virtual environment (recommended)
python3 -m venv ~/da3_venv
source ~/da3_venv/bin/activate

# Install PyTorch with CUDA support
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121

# Install other dependencies
pip3 install transformers>=4.35.0 \
  huggingface-hub>=0.19.0 \
  opencv-python>=4.8.0 \
  pillow>=10.0.0 \
  numpy>=1.24.0 \
  timm>=0.9.0

# Install ByteDance DA3 Python API (pip handles cloning automatically)
# This provides the model inference code, NOT the pre-trained weights
# Model weights will download from Hugging Face Hub on first run
pip3 install git+https://github.com/ByteDance-Seed/Depth-Anything-3.git

Note: For CPU-only systems, install PyTorch without CUDA:

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu

Step 3: Clone and Build This ROS2 Wrapper

# Navigate to your ROS2 workspace
cd ~/ros2_ws/src  # Or create: mkdir -p ~/ros2_ws/src && cd ~/ros2_ws/src

# Clone THIS ROS2 wrapper repository (not the ByteDance DA3 repo)
git clone https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper.git

# Build the package
cd ~/ros2_ws
colcon build --packages-select depth_anything_3_ros2

# Source the workspace
source install/setup.bash

Step 4: Verify Installation

# Test that the package is found
ros2 pkg list | grep depth_anything_3_ros2

# Run tests (optional)
colcon test --packages-select depth_anything_3_ros2
colcon test-result --verbose

Step 5: Pre-Download Models (Optional but Recommended)

Pre-download models to avoid delays on first run. This step is REQUIRED if deploying to offline robots.

# Download a specific model (requires internet connection)
python3 -c "
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
print('Downloading DA3-BASE model...')
AutoImageProcessor.from_pretrained('depth-anything/DA3-BASE')
AutoModelForDepthEstimation.from_pretrained('depth-anything/DA3-BASE')
print('Model cached to ~/.cache/huggingface/hub/')
print('You can now run offline!')
"

# For offline robots, copy the cache:
# tar -czf da3_models.tar.gz -C ~/.cache/huggingface .
# Transfer da3_models.tar.gz to robot and extract:
# tar -xzf da3_models.tar.gz -C ~/.cache/huggingface/

Alternative models:

For faster inference: Replace DA3-BASE with DA3-SMALL
For best quality: Replace DA3-BASE with DA3-LARGE

See Dependencies and Model Downloads for complete offline deployment instructions.

Quick Start

Single Camera (Generic USB Camera)

The fastest way to get started is with a standard USB camera:

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

# Terminal 2: Launch Depth Anything 3
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: Visualize with RViz2
rviz2 -d $(ros2 pkg prefix depth_anything_3_ros2)/share/depth_anything_3_ros2/rviz/depth_view.rviz

Using Pre-Built Example Launch Files

# USB camera example (requires v4l2_camera)
ros2 launch depth_anything_3_ros2 usb_camera_example.launch.py

# Static image test (requires image_publisher)
ros2 launch depth_anything_3_ros2 image_publisher_test.launch.py \
  image_path:=/path/to/your/test_image.jpg

Configuration

Parameters

All parameters can be configured via launch files or command line:

Parameter	Type	Default	Description
`model_name`	string	`depth-anything/DA3-BASE`	Hugging Face model ID or local path
`device`	string	`cuda`	Inference device (`cuda` or `cpu`)
`cache_dir`	string	`""`	Model cache directory (empty for default)
`inference_height`	int	`518`	Height for inference (model input)
`inference_width`	int	`518`	Width for inference (model input)
`input_encoding`	string	`bgr8`	Expected input encoding (`bgr8` or `rgb8`)
`normalize_depth`	bool	`true`	Normalize depth to [0, 1] range
`publish_colored`	bool	`true`	Publish colorized depth visualization
`publish_confidence`	bool	`true`	Publish confidence map
`colormap`	string	`turbo`	Colormap for visualization
`queue_size`	int	`1`	Subscriber queue size
`log_inference_time`	bool	`false`	Log performance metrics

Available Models

Model	Parameters	Use Case
`depth-anything/DA3-SMALL`	0.08B	Fast inference, lower accuracy
`depth-anything/DA3-BASE`	0.12B	Balanced performance (recommended)
`depth-anything/DA3-LARGE`	0.35B	Higher accuracy
`depth-anything/DA3-GIANT`	1.15B	Best accuracy, slower
`depth-anything/DA3NESTED-GIANT-LARGE`	Combined	Metric scale reconstruction

Topics

Subscribed Topics

~/image_raw (sensor_msgs/Image): Input RGB image from camera
~/camera_info (sensor_msgs/CameraInfo): Optional camera intrinsics

Published Topics

~/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

Usage Examples

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

# Launch everything together
ros2 launch depth_anything_3_ros2 usb_camera_example.launch.py \
  video_device:=/dev/video0 \
  model_name:=depth-anything/DA3-BASE

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):

# 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 4 cameras simultaneously:

# Launch multi-camera setup
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

Example 5: Testing with Static Images

Test with a static image using image_publisher:

sudo apt install ros-humble-image-publisher

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: Using Different Models

Switch between models for different performance/accuracy tradeoffs:

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

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

Example 7: CPU-Only Mode

Run on systems without CUDA:

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

Example 8: Custom Configuration

Use a custom parameter file:

# 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
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

Docker Deployment

Docker configuration files are provided for building and deploying on both CPU and GPU systems.

Important: No pre-built Docker images are published to Docker Hub or any container registry. You must build the images locally using docker-compose build or docker-compose up (which auto-builds).

Complete Docker Installation (3 Steps)

# Step 1: Clone the repository
git clone https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper.git
cd GerdsenAI-Depth-Anything-3-ROS2-Wrapper

# Step 2: Build and run (choose GPU or CPU)
docker-compose up -d depth-anything-3-gpu    # For GPU (requires nvidia-docker)
# OR
docker-compose up -d depth-anything-3-cpu    # For CPU-only

# Step 3: Enter container and run the node
docker exec -it da3_ros2_gpu bash            # For GPU container
# OR
docker exec -it da3_ros2_cpu bash            # For CPU container

# Inside the container:
ros2 run depth_anything_3_ros2 depth_anything_3_node --ros-args -p device:=cuda

Quick Start with Docker Compose

# CPU-only mode
docker-compose up -d depth-anything-3-cpu
docker exec -it da3_ros2_cpu bash

# GPU mode (requires nvidia-docker)
docker-compose up -d depth-anything-3-gpu
docker exec -it da3_ros2_gpu bash

# Development mode (source mounted)
docker-compose up -d depth-anything-3-dev

Manual Docker Build

# Build GPU image
docker build -t depth_anything_3_ros2:gpu \
    --build-arg BUILD_TYPE=cuda-base \
    .

# Run with USB camera
docker run -it --rm \
    --runtime=nvidia \
    --gpus all \
    --network host \
    --privileged \
    -v /dev:/dev:rw \
    depth_anything_3_ros2:gpu

Pre-configured Services

The docker-compose.yml includes:

depth-anything-3-cpu: CPU-only deployment
depth-anything-3-gpu: GPU-accelerated deployment
depth-anything-3-dev: Development environment
depth-anything-3-usb-camera: Standalone USB camera service

Docker Testing and Validation

Automated test suite for validating Docker images:

cd docker
chmod +x test_docker.sh
./test_docker.sh

This comprehensive test suite validates:

Docker and Docker Compose installation
CPU and GPU image builds
ROS2 installation and package builds
Python dependencies
CUDA availability (GPU images)
Volume mounts and networking
Model download capability

For detailed Docker documentation, see docker/README.md.

Example Images and Benchmarks

Sample Test Images

Download sample images for quick testing:

cd examples
./scripts/download_samples.sh

This downloads sample indoor, outdoor, and object images from public datasets.

Testing with Static Images

# Test single image
python3 examples/scripts/test_with_images.py \
    --image examples/images/outdoor/street_01.jpg \
    --model depth-anything/DA3-BASE \
    --device cuda \
    --output-dir results/

# Batch process directory
python3 examples/scripts/test_with_images.py \
    --input-dir examples/images/outdoor/ \
    --output-dir results/ \
    --model depth-anything/DA3-BASE

Performance Benchmarking

Run comprehensive benchmarks across multiple models and image sizes:

# Benchmark multiple models
python3 examples/scripts/benchmark.py \
    --images examples/images/ \
    --models depth-anything/DA3-SMALL,depth-anything/DA3-BASE,depth-anything/DA3-LARGE \
    --sizes 640x480,1280x720 \
    --device cuda \
    --output benchmark_results.json

Example output:

================================================================================
BENCHMARK SUMMARY
================================================================================
Model                          Device   Size         FPS      Time (ms)    GPU Mem (MB)
--------------------------------------------------------------------------------
depth-anything/DA3-SMALL       cuda     640x480      25.3     39.5         1512
depth-anything/DA3-BASE        cuda     640x480      19.8     50.5         2489
depth-anything/DA3-LARGE       cuda     640x480      11.7     85.4         3952
================================================================================

Advanced Example Scripts

Depth Post-Processing

Apply filtering, hole filling, and enhancement to depth maps:

cd examples/scripts

# Process single depth map
python3 depth_postprocess.py \
    --input depth.npy \
    --output processed.npy \
    --visualize

# Batch process directory
python3 depth_postprocess.py \
    --input depth_dir/ \
    --output processed_dir/ \
    --batch

Multi-Camera Synchronization

Synchronize depth estimation from multiple cameras:

# Terminal 1: Launch multi-camera setup
ros2 launch depth_anything_3_ros2 multi_camera.launch.py \
    camera_namespaces:=cam_left,cam_right \
    image_topics:=/cam_left/image_raw,/cam_right/image_raw

# Terminal 2: Run synchronizer
python3 multi_camera_sync.py \
    --cameras cam_left cam_right \
    --sync-threshold 0.05 \
    --output synchronized_depth/

TensorRT Optimization (Jetson)

Optimize models for maximum performance on Jetson platforms:

# Optimize model
python3 optimize_tensorrt.py \
    --model depth-anything/DA3-BASE \
    --output da3_base_trt.pth \
    --precision fp16 \
    --benchmark

# Expected speedup: 2-3x faster inference

Performance Tuning

Quantization, ONNX export, and profiling:

# INT8 quantization
python3 performance_tuning.py quantize \
    --model depth-anything/DA3-BASE \
    --output da3_base_int8.pth

# Export to ONNX
python3 performance_tuning.py export-onnx \
    --model depth-anything/DA3-BASE \
    --output da3_base.onnx \
    --benchmark

# Profile layers
python3 performance_tuning.py profile \
    --model depth-anything/DA3-BASE \
    --layers \
    --memory

ROS2 Batch Processing

Process ROS2 bags through depth estimation:

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

Node Profiling

Profile ROS2 node performance:

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

For more examples, see examples/README.md.

Documentation

Complete documentation is available in multiple formats:

Sphinx Documentation

Build and view the complete API documentation:

cd docs
pip install -r requirements.txt
make html
open build/html/index.html  # or xdg-open on Linux

Documentation Contents

API Reference: Complete API documentation with examples
User Guides:
- Installation and setup
- Camera integration guide
- Multi-camera configuration
- Performance optimization
- Troubleshooting
Tutorials:
- Quick Start Tutorial - Get up and running in minutes
- USB Camera Setup - Complete USB camera guide
- Multi-Camera Setup - Synchronized multi-camera depth
- Performance Tuning - Optimization guide for all platforms

Additional Documentation

Performance

Benchmark Results (Jetson Orin AGX 64GB)

Tested with 640x480 input images:

Model	FPS	Inference Time	GPU Memory
DA3-Small	~25 FPS	~40ms	~1.5 GB
DA3-Base	~20 FPS	~50ms	~2.5 GB
DA3-Large	~12 FPS	~85ms	~4.0 GB
DA3-Giant	~6 FPS	~165ms	~6.5 GB

Optimization Tips

TensorRT Optimization (Jetson platforms):

cd examples/scripts
python3 optimize_tensorrt.py --model depth-anything/DA3-BASE \
    --output da3_base_trt.pth --precision fp16
# Expected: 2-3x speedup

INT8 Quantization for faster inference:

python3 performance_tuning.py quantize \
    --model depth-anything/DA3-BASE --output da3_base_int8.pth
# 50-75% smaller, 20-40% faster

Reduce Input Resolution: Lower resolution images process faster

--param inference_height:=384 inference_width:=512

Use Smaller Models: DA3-SMALL offers best speed, DA3-BASE balances speed/accuracy
Queue Size: Set to 1 to always process latest frame

--param queue_size:=1

Disable Unused Outputs: Save processing time

--param publish_colored_depth:=false
--param publish_confidence:=false

Multiple Cameras: Each camera runs in separate process with shared GPU
Performance Profiling: Profile to identify bottlenecks

python3 examples/scripts/profile_node.py --model depth-anything/DA3-BASE

For comprehensive optimization guide, see Performance Tuning Tutorial.

Troubleshooting

Common Issues

1. Model Download Failures

Error: Failed to load model from Hugging Face Hub or Connection timeout

Solutions:

Check internet connection: ping huggingface.co
Verify Hugging Face Hub is accessible: May be blocked by firewall/proxy

Pre-download models manually:

python3 -c "from transformers import AutoImageProcessor, AutoModelForDepthEstimation; \
            AutoImageProcessor.from_pretrained('depth-anything/DA3-BASE'); \
            AutoModelForDepthEstimation.from_pretrained('depth-anything/DA3-BASE')"

Use custom cache directory: Set HF_HOME=/path/to/models environment variable
For offline robots: See Offline Operation section

2. Model Not Found on Offline Robot

Error: Model depth-anything/DA3-BASE not found on robot without internet

Solution: Pre-download models and copy cache directory:

# On development machine WITH internet:
python3 -c "from transformers import AutoModelForDepthEstimation; \
            AutoModelForDepthEstimation.from_pretrained('depth-anything/DA3-BASE')"
tar -czf da3_models.tar.gz -C ~/.cache/huggingface .

# Transfer to robot (USB, SCP, etc.) and extract:
ssh robot@robot-ip
mkdir -p ~/.cache/huggingface
tar -xzf da3_models.tar.gz -C ~/.cache/huggingface/

Verify models are available:

ls ~/.cache/huggingface/hub/models--depth-anything--*

3. CUDA Out of Memory

Error: RuntimeError: CUDA out of memory

Solutions:

Use a smaller model (DA3-Small or DA3-Base)
Reduce input resolution
Close other GPU applications
Switch to CPU mode temporarily

# Use smaller model
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  model_name:=depth-anything/DA3-SMALL

# Or use CPU
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  device:=cpu

2. Model Download Failures

Error: Failed to load model from Hugging Face Hub

Solutions:

Check internet connection
Verify Hugging Face Hub is accessible
Download model manually and use local path

# Download manually
python3 -c "from huggingface_hub import snapshot_download; snapshot_download('depth-anything/DA3-BASE')"

# Use local path
ros2 launch depth_anything_3_ros2 depth_anything_3.launch.py \
  model_name:=/path/to/local/model

3. Image Encoding Mismatches

Error: CV Bridge conversion failed

Solutions:

Check camera's output encoding
Adjust input_encoding parameter

# For RGB cameras
--param input_encoding:=rgb8

# For BGR cameras (most common)
--param input_encoding:=bgr8

4. No Image Received

Solutions:

Verify camera is publishing: ros2 topic echo /camera/image_raw
Check topic remapping is correct
Verify QoS settings match camera

# List available topics
ros2 topic list | grep image

# Check topic info
ros2 topic info /camera/image_raw

5. Low Frame Rate

Solutions:

Check GPU utilization: nvidia-smi
Enable performance logging
Reduce image resolution
Use smaller model

# Enable performance logging
--param log_inference_time:=true

Development

Running Tests

# Run all tests
cd ~/ros2_ws
colcon test --packages-select depth_anything_3_ros2

# View test results
colcon test-result --verbose

# Run specific test
python3 -m pytest src/depth_anything_3_ros2/test/test_inference.py -v

Code Style

This package follows:

PEP 8 for Python code
Google-style docstrings
Type hints for all functions
No emojis in code or documentation

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Follow code style guidelines
Add tests for new functionality
Submit a pull request

Citation

If you use Depth Anything 3 in your research, please cite the original paper:

@article{depthanything3,
  title={Depth Anything 3: A New Foundation for Metric and Relative Depth Estimation},
  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
  journal={arXiv preprint arXiv:2511.10647},
  year={2024}
}

License

This ROS2 wrapper is released under the MIT License.

The Depth Anything 3 model has its own license. Please refer to the official repository for model license information.

Support

Issues: GitHub Issues
Discussions: GitHub Discussions
ROS2 Documentation: ROS2 Humble Docs
Depth Anything 3: Official Repository

Note: This is an unofficial ROS2 wrapper. For the official Depth Anything 3 implementation, please visit the ByteDance-Seed repository.

Name		Name	Last commit message	Last commit date
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.github		.github
config		config
depth_anything_3_ros2		depth_anything_3_ros2
docker		docker
docs		docs
examples		examples
launch		launch
resource		resource
rviz		rviz
scripts		scripts
test		test
.dockerignore		.dockerignore
.flake8		.flake8
.gitignore		.gitignore
ACKNOWLEDGEMENTS.md		ACKNOWLEDGEMENTS.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
OPTIMIZATION_GUIDE.md		OPTIMIZATION_GUIDE.md
README.md		README.md
VALIDATION_CHECKLIST.md		VALIDATION_CHECKLIST.md
docker-compose.yml		docker-compose.yml
mypy.ini		mypy.ini
package.xml		package.xml
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.py		setup.py

License

GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper

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WORK IN PROGRESS LOOKING FOR CONTRIBUTERS :)

Depth Anything 3 ROS2 Wrapper

Acknowledgments and Credits

Depth Anything 3

Inspiration from Prior ROS2 Wrappers

Overview

Key Features

Supported Platforms

Important: Dependencies and Model Downloads

What Gets Installed

Offline Operation (Robots Without Internet)

Table of Contents

Installation

Prerequisites

Step 1: Install ROS2 Dependencies

Step 2: Install Python Dependencies

Step 3: Clone and Build This ROS2 Wrapper

Step 4: Verify Installation

Step 5: Pre-Download Models (Optional but Recommended)

Quick Start

Single Camera (Generic USB Camera)

Using Pre-Built Example Launch Files

Configuration

Parameters

Available Models

Topics

Subscribed Topics

Published Topics

Usage Examples

Example 1: Generic USB Camera (v4l2_camera)

Example 2: ZED Stereo Camera

Example 3: Intel RealSense Camera

Example 4: Multi-Camera Setup

Example 5: Testing with Static Images

Example 6: Using Different Models

Example 7: CPU-Only Mode

Example 8: Custom Configuration

Docker Deployment

Complete Docker Installation (3 Steps)

Quick Start with Docker Compose

Manual Docker Build

Pre-configured Services

Docker Testing and Validation

Example Images and Benchmarks

Sample Test Images

Testing with Static Images

Performance Benchmarking

Advanced Example Scripts

Depth Post-Processing

Multi-Camera Synchronization

TensorRT Optimization (Jetson)

Performance Tuning

ROS2 Batch Processing

Node Profiling

Documentation

Sphinx Documentation

Documentation Contents

Additional Documentation

Performance

Benchmark Results (Jetson Orin AGX 64GB)

Optimization Tips

Troubleshooting

Common Issues

1. Model Download Failures

2. Model Not Found on Offline Robot

3. CUDA Out of Memory

2. Model Download Failures

3. Image Encoding Mismatches

4. No Image Received

5. Low Frame Rate

Development

Running Tests

Code Style

Contributing

Packages