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.
LongCat-Flash-Omni is an application providing inference capabilities for a 560B parameter omni-modal model that processes text, audio, image, and video inputs to generate text and/or audio outputs. The system features a modular architecture with separate modality encoders (audio, vision), a unified data processor for multimodal inputs, and a post-processor for converting model outputs to usable formats. The main entry point is longcat_omni_demo.py which orchestrates the inference pipeline, leveraging SGLang for efficient model serving. The repository demonstrates strong engineering practices in its modular design, particularly in the audio feature processing and image transformation components.
Helios is a real-time long video generation system that implements text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) generation using a 3D transformer architecture. The system features a modular design with specialized optimizations for performance including Ahead-of-Time Compilation (AOTI), group offloading for VRAM reduction, context parallelism (Ulysses/Ring attention), and Flash Attention kernel optimizations. It employs a multi-stage training pipeline (Base → Mid → Distilled) to balance quality and sampling speed. The repository includes a Gradio web interface (app.py) for interactive generation, command-line inference scripts, training utilities, and a comprehensive evaluation suite measuring video quality across aesthetic, motion, semantic, and naturalness dimensions. While containing research elements for ablation studies and benchmarking, its primary function is as a deployable video generation application.