#COLMAP

The BAD-Gaussians project leverages the nerfstudio framework to implement Gaussian-based methods for effective deblurring and novel-view synthesis of motion-blurred images. This approach optimizes the Deblur-NeRF datasets by enhancing clarity and rendering quality through robust Gaussian splatting techniques. It supports both synthetic and real-world data processing with adjustable parameters for camera pose interpolation and image scaling, offering comprehensive instructions for installation, dataset setup, training, video rendering, and 3D Gaussian export. This makes it a valuable resource for researchers and developers in the fields of image processing and computer vision.

Explore neural surface reconstruction techniques using NVIDIA's Imaginaire library. This project guides setting up environments with Docker and Conda for efficient execution. Learn methods for video frame data preparation, CUDA-based accelerated mesh processing, and optimizing isosurface extraction to improve 3D model quality with adaptable configuration options.

Explore a modular framework facilitating the advancement and development of 3D Gaussian Splatting (3DGS) technologies. The framework supports diverse lighting, complex geometries, and includes comprehensive datasets and tools for installation and mesh extraction. With extensive data compatibility and a focus on 3DGS methodology expansion, it aids in developing effective solutions for modeling a range of scenes.

GLOMAP introduces an efficient image-based reconstruction pipeline that accelerates global structure-from-motion processes significantly, surpassing traditional methods. It offers high-quality 3D reconstruction using COLMAP databases, improving efficiency exponentially. Seamlessly integrating with COLMAP, PoseLib, and multiple visualization tools, GLOMAP is well-suited for researchers requiring fast and accurate 3D modeling. Leverage GLOMAP to enhance image processing across expansive datasets, ensuring high-quality reconstruction for advanced use cases.