Omkar2118/datasets huggingface dataset:
NLB
Full name: Neural Latents Benchmark

**Neural Latents** is a benchmark for latent variable modeling of neural population activity. It consists of four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.

Introducing paper: Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
HM3D
Full name: Habitat-Matterport 3D

**Habitat-Matterport 3D** (HM3D) is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and other private indoor spaces.

HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual fidelity. HM3D contains 112.5k m^2 of navigable space, which is 1.4 - 3.7x larger than other building-scale datasets such as [MP3D](matterport3d) and [Gibson](gibson-environment). When compared to existing photorealistic 3D datasets such as [Replica](replica), MP3D, Gibson, and [ScanNet](scannet), images rendered from HM3D have 20 - 85% higher visual fidelity w.r.t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction.

Introducing paper: Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI
Depth VIDIT
Full name: Virtual Image Dataset for Illumination Transfer

VIDIT is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. Virtual datasets are not only an important step towards achieving real-image performance but have also proven capable of improving training even when real datasets are possible to acquire and available. VIDIT contains 300 virtual scenes used for training, where every scene is captured 40 times in total: from 8 equally-spaced azimuthal angles, each lit with 5 different illuminants.

Introducing paper: NTIRE 2021 Depth Guided Image Relighting Challenge
ADEFAN

This data set contains 50 low resolution (640 x 360) short videos containing a variety real life activities.

Introducing paper: Divergence-Based Adaptive Extreme Video Completion