Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

Synthetic datasets, for which we propose the term synthsets, are not a novelty but have
become a necessity. Although they have been used in computer vision since 1989, hel** …

We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural
world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from …

We have witnessed significant progress in deep learning-based 3D vision ranging from
neural radiance field (NeRF) based 3D representation learning to applications in novel view …

Computer vision now relies on data, but we know surprisingly little about what factors in the
data affect performance. We argue that this stems from the way data is collected. Designing …

Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-
pixel ground truth labels from real images. We address this challenge by introducing …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding
algorithms. However, existing datasets still cover only a limited number of views or a …

Abstract We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …