Mike Roberts

TL;DR: In this paper, the authors describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many subcellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database.

TL;DR: In this paper, a random forest classifier is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image, which are then combined into geometrically consistent 3D objects by segmentation fusion.

TL;DR: This work introduces Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding, and finds that it is possible to generate the entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model.

TL;DR: In this article, the authors present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model.

TL;DR: The tool makes it possible for novices and experts to design compelling and challenging shots, and capture them fully autonomously, and is evaluated in a user study with novice and expert cinematographers.