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Tanks and Temples
Neural Radiance Fields (NeRFs) model a 3D scene as a volumetric function, which can be rendered from arbitrary viewpoints to generate highly-realistic images. -
BABEL dataset
The dataset used in this paper is the BABEL dataset, which contains 10881 motion sequences, with 65926 subsequences and the corresponding textual labels. -
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Human motion generation includes the intuitive description, editing, and generation of 3D sequences of human poses. It is relevant to many applications that require virtual or... -
MipNeRF 360
MipNeRF 360 dataset -
DTU MVS Dataset and Local Light Field Fusion Dataset
The DTU MVS Dataset and the Local Light Field Fusion Dataset are used to evaluate the performance of the proposed GARF model. -
ScanNet Dataset
The ScanNet dataset is a large-scale indoor dataset composed of monocular sequences with ground truth poses and depth images. -
Habitat-Matterport 3D (HM3D) Dataset
Habitat-Matterport 3D (HM3D) dataset includes realistc scans of 1,000 buildings. -
Gibson Dataset
Gibson dataset includes realistic scans of 572 full buildings. -
Human Body dataset
Human Body dataset is a benchmark for 3D shape segmentation. -
COSEG dataset
COSEG dataset is a benchmark for 3D shape segmentation. -
Cube Engraving dataset
Cube Engraving dataset is a benchmark for 3D shape classification. -
BlendedMVS
The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World -
Synthetic-NSVF
The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World -
Synthetic-NeRF
The dataset used in the paper SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World -
Scene Flow and KITTI2015 datasets
Two publicly available datasets for training and testing: Scene Flow datasets and KITTI2015 dataset