#3D Gaussian Splatting

Discover how the collaboration with LLM-Agent enhances autonomous driving simulations by advancing editable scene rendering. This project leverages 3D Gaussian splatting to drastically speed up background rendering, achieving remarkable efficiency. Improved Blender processes allow rapid foreground rendering, showcasing significant technological advancements. Supported by OpenAI and NVIDIA AI, this simulation project offers enhanced rendering quality and speed. Explore the innovative features that make this a pivotal tool for autonomous driving development.

This platform presents an organized collection of 3D Gaussian Splatting research, including papers and reviews from leading conferences like CVPR2024 and ECCV2024. It highlights research across various institutions, offering insights into applications such as robotics and 3D reconstruction. The repository includes resources like code and abstracts, supporting further exploration. This centralized database helps users stay updated on advancing technologies in 3D Gaussian Splatting.

Discover a broad array of resources on 3D Gaussian Splatting, featuring progress in areas such as 3D object detection and autonomous driving. This compilation provides access to cutting-edge methods, open-source projects, and educational materials, reflecting current research developments. Contributions are welcomed to further enrich this knowledge base. Engage with the community through blog articles and multimedia content, while staying updated on emerging research and projects.

This project presents an innovative method for real-time rendering of radiance fields, emphasizing 1080p high-resolution novel-view synthesis without speed sacrifice. It utilizes 3D Gaussian models based on sparse data from camera calibration to enhance scene representation with anisotropic covariance. The framework incorporates a visibility-aware rendering algorithm supporting anisotropic splatting to improve both training efficiency and real-time rendering. Recent updates have introduced accelerated training and OpenXR support, making this tool valuable for both researchers and those new to efficient real-time rendering.

HeadStudio uses 3D Gaussian splatting to transform text into animatable head avatars. Developed by ReLER at Zhejiang University, it supports animation from video, audio, and text inputs. Utilizing frameworks like FLAME and technologies such as PlayHT and TalkSHOW, it offers tools for creating digital avatars. HeadStudio supports research and application with its integration capabilities for animating avatars.

Discover an approach for 3D Gaussian splatting with sparse multi-view images, showcasing efficiency and precision at ECCV 2024. This method uses minimal views for high-quality 3D reconstruction, suitable for realistic rendering and vision applications. It employs datasets like RealEstate10K and ACID, versatile with single A100 GPU training. Comprehensive scripts aid data preparation, model evaluation, and training customization, highlighting cross-dataset performance across varied settings for reliable 3D rendering capabilities.

The project offers a unique 3D smoothing and 2D Mip filter for Gaussian Splatting, eliminating artifacts for clean renderings. With enhancements from a densification metric, it improves view synthesis outcomes. Detailed setup instructions and rich dataset support, including Blender and Mip-NeRF 360 datasets, ensure robust experimentation and model evaluation.

This tool allows for real-time exploration and editing of 3D Gaussian Splatting scenes. Utilizing a Python GUI library, it facilitates immediate manipulation and evaluation of scenes, offering features like multiscreen views and scene comparisons. Users can export renderings and videos, with options for live training monitoring. Installation is straightforward via Conda or Micromamba. The platform is compatible with Windows and Linux, making it suitable for developers and creators interested in 3D scene debugging and visualization.

The GPS-Gaussian project presents a flexible 3D Gaussian method for real-time generation of novel views of unseen characters without extra adjustments. Perfect for scenarios demanding quick multi-angle human renderings, it involves straightforward installation and can be optimized with CUDA to enhance speed. It supports strong training with extensive datasets and includes pretrained models for fast testing. This facilitates rapid deployment on synthetic as well as actual world data, ensuring seamless novel view rendering.

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.