Filed by Gores Holdings VI, Inc.
pursuant to Rule 425 under the Securities Act of 1933
and deemed filed pursuant to Rule 14a-12
under the Securities Exchange Act of 1934
Subject Company: Gores Holdings VI, Inc.
Commission File No.: 001-39790
Date: June 30, 2021
Matterport and Facebook AI Research Collaborate to Release the Worlds Largest Dataset of 3D Spaces for Academic
Research
The collaboration enables researchers to advance Habitat, Facebook AIs simulation platform for research in Embodied AI, to help
robots better understand and interact with the physical world.
SUNNYVALE, Calif. Matterport, the spatial data company leading the digital
transformation of the built world, which has entered into a definitive agreement to enter into a business combination with Gores Holdings VI (NASDAQ: GHVI, GHVIU, and GHVIW, today announced a collaboration with Facebook AI Research (FAIR) through
which it will make the largest-ever dataset of 3D indoor spaces available exclusively for academic, non-commercial uses. The Habitat-Matterport 3D Research Dataset (HM3D) is an unprecedented collection of
1,000 high-resolution Matterport digital twins made up of residential, commercial, and civic spaces generated precisely from real-world environments. HM3D will play a significant role in advancing embodied AI research which seeks to teach robots and
virtual AI assistants to understand and interact with the complexities of the physical world.
Until now, this rich spatial data has been glaringly
absent in the field, so HM3D has the potential to change the landscape of embodied AI and 3D computer vision, said Dhruv Batra, Research Scientist at Facebook AI Research. Our hope is that the 3D dataset brings researchers closer to
building intelligent machines, to do for embodied AI what pioneers before us did for 2D computer vision and other areas of AI.
HM3D is free and
available now for academic, non-commercial research. Researchers can use it with FAIRs Habitat simulator to train embodied agents, such as home robots and AI assistants, at scale. HM3D is a
foundational step towards helping these agents navigate through real-world environments and better understand the variations of spaces such as bedrooms, bathrooms, kitchen and hallways, as well as the different configurations of those rooms within
every structure. It can also assist robots in recognizing how objects within rooms are typically arranged so that instructions are correctly understood. This research could one day be used in production applications like robots that can retrieve
medicine from a bedroom nightstand or AR glasses that can help people remember where they left their keys.