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DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the D3 Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research.

Structured3D is a large-scale photo-realistic dataset containing 3.5K house designs (a) created by professional designers with a variety of ground truth 3D structure annotations (b) and generate photo-realistic 2D images (c). The dataset consists of rendering images and corresponding ground truth annotations (e.g., semantic, albedo, depth, surface normal, layout) under different lighting and furniture configurations. Source: https://github.com/bertjiazheng/Structured3D Image Source: https://github.com/bertjiazheng/Structured3D

The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. Source: Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset

D3D-HOI is a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. The dataset consists of several common articulated objects captured from diverse real-world scenes and camera viewpoints. Each manipulated object (e.g., microwave oven) is represented with a matching 3D parametric model. This data allows researchers to evaluate the reconstruction quality of articulated objects and establish a benchmark for this challenging task. Image source: https://github.com/facebookresearch/d3d-hoi

CWD30 comprises over 219,770 high-resolution images of 20 weed species and 10 crop species, encompassing various growth stages, multiple viewing angles, and environmental conditions. The images were collected from diverse agricultural fields across different geographic locations and seasons, ensuring a representative dataset.

We introduce the SHAD3S dataset, that for a given contour representation of a mesh, under a given illumination condition, provides the illumination masks on the object, a shadow mask on the ground, its diffuse and sketch renders. Dataset creation code

The D3DFACS dataset is a dynamic 3D facial expression data set based on the Facial Action Coding System. It contains Action Unit (AU) sequences from 10 people, with 519 sequences in total. The peak image of each expression sequence has been manually FACS coded by a certified expert. Registered meshes in FLAME mesh topology are available under https://flame.is.tue.mpg.de/downloads

The semantic segmentation of clothes is a challenging task due to the wide variety of clothing styles, layers and shapes. The UTFPR-SBD3 contains 4,500 images manually annotated at pixel level in 18 classes plus background. To ensure the high quality of the dataset, all images were manually annotated at the pixel level using JS Segment Annotator, 2 a free web-based image annotation tool. The raw images were carefully selected to avoid, as far as possible, classes with low number of instances.