78 datasets found

Generating geometric 3D reconstructions from Neural Radiance Fields (NeRFs) is of great interest. However, accurate and complete reconstructions based on the density values are...

The Real Forward-Facing dataset is a dataset for training neural radiance fields.

The Realistic Synthetic NeRF dataset is a dataset for training neural radiance fields.

Neural Radiance Fields (NeRFs) model a 3D scene as a volumetric function, which can be rendered from arbitrary viewpoints to generate highly-realistic images.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

D-NeRF: Neural Radiance Fields for Dynamic Scenes

Real-world multi-view datasets DTU and LLFF for view synthesis from sparse inputs.

AvatarVerse is a stable pipeline for generating expressive high-quality 3D avatars from text descriptions and pose guidance.

An N-object dataset for testing the proposed framework

The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

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 DyNeRF dataset contains 3D dynamic scenes with moving or deforming objects.

The MeetRoom dataset contains 3D dynamic scenes with moving or deforming objects.

The D-NeRF dataset contains 3D dynamic scenes with moving or deforming objects.

The Mip-NeRF360 dataset contains scenes taken from a 360 degree view, with emphasis on minimizing photometric variations.

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...