A ROS 2 node that performs real-time metric depth estimation and point cloud generation using Depth Anything V3 accelerated with TensorRT. The system subscribes to camera image and camera info topics, synchronizes them via approximate time, runs GPU-accelerated preprocessing and inference, then publishes a 32-bit float depth image and a colorized or monochrome point cloud. The pipeline includes GPU-side bicubic upscaling, focal-length-based metric scaling, sky detection with percentile-based fill, and optional debug visualization with FPS overlay. The repository provides a complete build system (ament_cmake, CUDA, TensorRT), a standalone engine generation tool, a Docker image, and an ONNX export script for the underlying PyTorch model. It is designed for autonomous driving and robotics perception applications requiring high frame rates (claimed 50 FPS on Quadro RTX 6000). The architecture is a monolithic ROS 2 component with a clean separation between the node, the TensorRT wrapper, and the GPU kernels.
Depth Anything V2 is a research codebase for monocular depth estimation, providing both relative and metric depth models. It builds on a DINOv2 encoder with a DPT decoder, offering four model scales (Small, Base, Large, Giant). The repository includes inference scripts for images and videos, a Gradio demo, and a subdirectory for fine-tuning on metric depth estimation using Hypersim (indoor) and Virtual KITTI 2 (outdoor) datasets. It also defines the DA-2K evaluation benchmark for relative depth. The code is well-structured with reusable transform utilities and evaluation metrics, but reproducibility is limited due to hardcoded dataset paths and unpinned dependencies. This is primarily a research project with a hybrid nature due to the included demo application.