2024年3月15日 · This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.

2024年4月1日 · GGRt 实现了令人印象深刻的推理和实时渲染速度，优于现有的无姿态技术，并接近基于姿态的 3D-GS 方法。 在多样化数据集上进行了大量实验，证实了其有效性。

Official implementation of "GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time". For LLFF dataset, please follow the ibrnet. For Waymo dataset, please download the data from EmerNeRF. The data structure is as follows: │ │ │ ├── ... │ ├── ... │ │ ├── ... we provide launch.json formatted for debugging. "env": {

2024年11月1日 · This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.

本文提出了一种新的联合学习框架，包括迭代姿态优化网络（IPO-Net）和通用3D高斯模型（G-3DG），该框架可以从图像观测中固有地估计出强韧的相对姿态信息，从而主要减轻了对实际相机姿态的要求。

This paper presents GGRt, a novel approach to generaliz-able novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimiza-tion processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.

2024年3月18日 · 本文提出了 GGRt，这是一种可推广的新颖视图合成的新方法，可减轻对真实相机姿势的需求、处理高分辨率图像的复杂性以及冗长的优化过程，从而促进 3D 高斯泼溅（3D-GS）在现实中具有更强的适用性 -世界场景。

This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.

As the first pose-free generalizable 3D-GS framework, GGRt achieves inference at 5 FPS and real-time rendering at 100 FPS. Through extensive experimentation, we demonstrate that our method outperforms existing NeRF-based pose-free techniques in …

As the first pose-free generalizable 3D-GS framework, GGRt achieves inference at ≥ ≥ 5 FPS and real-time rendering at ≥ ≥ 100 FPS. Through extensive experimentation, we demonstrate that our method outperforms existing NeRF-based pose-free techniques in …