11 datasets found

The Mip-NeRF 360 and Tanks & Temples datasets are used to evaluate the performance of the Pixel-GS method.

EscherNet is a multi-view conditioned diffusion model designed for scalable view synthesis. It leverages Stable Diffusion's 2D architecture empowered by the innovative Camera...

A neural scene flow fields for space-time view synthesis of dynamic scenes.

The dataset used in the paper is the LLFF dataset, which contains real-world scenes and is used for training and testing the proposed neural radiance field model.

The dataset used in the paper for omnidirectional Gaussian Splatting for fast radiance field reconstruction using omnidirectional images.

A dataset of synthetic photorealistic images for training and testing material-aware diffusion models

The dataset used in the paper for training and testing the NeRF model.

NeRF [33] has demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray...

The DTU dataset is a large-scale dataset for multi-view stereo depth inference. It contains over 100 scans taken under 7 different lighting conditions and fixed camera...

The DTU dataset is a large-scale dataset for 3D reconstruction and editing. It contains 15 scenes with images of 1600x1200 resolution and accompanying foreground masks.

The LLFF dataset contains 8 forward-facing scenes. Following [19, 26, 35, 42], we take every 8-th image as the novel views for testing. The input views are evenly sampled across...