SPAG4D is a production-grade tool that converts 360° equirectangular panoramic photos into explorable 3D Gaussian Splat (3DGS) scenes. It integrates multiple depth estimation backends (DA360, DAP, PaGeR, SHARP, UniSHARP) to generate depth maps from a single panorama, then projects the points into standard 3DGS PLY format. The system provides a CLI, a FastAPI web server with a JavaScript frontend, and a Python API for programmatic use. Beyond the core conversion, it includes optional refinement pipelines to fill holes and improve scene quality: OmniRoam (WSL2-based video generation and distillation), ArtiFixer3D (Docker-based diffusion refinement), and a lighter geometric refinement pipeline using depth alignment and voxel aggregation. The architecture is modular with pluggable generators, comprehensive configuration dataclasses, and provenance tracking. While the tool is primarily an application, it leans heavily on research-grade depth models and refinement techniques, giving it a hybrid character. The codebase is well-structured with clear separation of concerns, reusable utilities (spherical projection, depth blending, scene filtering, geometric alignment), and extensive tests. It is actively maintained with recent commits, though it carries complexity from multiple backends and platform-specific dependencies (WSL2, Docker, CUDA).
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.
TheAnimeScripter (TAS) is a production-grade AI video enhancement toolkit specialized for anime content. It performs frame interpolation (RIFE 4.15-4.25 variants, GMFSS, DistilDRBA), super-resolution (30+ architectures via a vendored spandrel fork), restoration, frame deduplication, monocular depth estimation (Depth Anything V2/V3, Video Depth Anything, Limbo), scene-change detection, stabilization, segmentation, and object detection. It ships as a CLI tool (main.py), an Adobe After Effects plugin (src/server/aeComms.py via Socket.IO), and a standalone desktop build (build.py + tools/build_support/). The pipeline is: decode video via FFmpeg or the custom nelux library, stream frames through a FrameWindow sliding buffer, apply dedup → restore → upscale → interpolate stages, then re-encode with configurable codecs, bit depths, and BT.709/BT.2020 color spaces.
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.
Scal3R is a research codebase for CVPR 2026 Highlight paper on large-scale 3D reconstruction from unordered images using test-time training. The core contribution is a novel bidirectional attention mechanism with fast weight updates (TTT layers) that adapts during inference to improve reconstruction quality for large scenes. The repository provides an end-to-end inference pipeline: given a folder of images, it estimates camera poses, depth maps, and produces a colored point cloud. The architecture builds on a DINOv2 backbone and VGGT-based aggregator with custom attention heads, loop detection via SALAD, and pose graph optimization with submap management. The codebase is well-structured with separate modules for data loading, model definition, inference orchestration, and visualization tools. It includes fallback implementations for key components (e.g., Python-only loop solver, simple correlation-based loop detector) to improve portability, though GPU and CUDA are assumed. The repository is primarily a research artifact with clean utilities that could be extracted for other 3D vision projects. Evaluation code and dataset scripts are explicitly marked as not yet released, limiting reproducibility until those are provided.
StereoCrafter is an application that converts monocular videos to stereoscopic 3D videos using a two-stage diffusion-based pipeline. The first stage estimates depth via DepthCrafter and generates a right-eye view through forward warping. The second stage refines the stereo video using a fine-tuned Stable Video Diffusion model with spatial tiling for high-resolution processing. The system relies on external dependencies including DepthCrafter and Forward-Warp submodules, and requires manual download of model weights. The codebase demonstrates modular design with clear separation between depth estimation, warping, and inpainting stages.
SpaTrackerV2 is a 3D point tracking system that processes video input to estimate camera motion, depth, and track user-selected points in 3D space. It integrates multiple depth estimation models (ZoeDepth, DepthAnything, DepthPro) with a transformer-based tracking architecture, offering both offline (full video) and online (sliding window) processing modes. The system provides a Gradio-based web interface for interactive point selection with SAM assistance and outputs 2D tracking videos, 3D visualizations (HTML), and numerical trajectory data. Evaluation modules support benchmarking against datasets like Sintel and CVO. The codebase demonstrates modular design with reusable components for I/O, geometry, alignment, and visualization, though it relies heavily on specific external models and libraries.