  With the rapid development of data-driven techniques, data has played an
essential role in various computer vision tasks. Many realistic and synthetic
datasets have been proposed to address different problems. However, there are
lots of unresolved challenges: (1) the creation of dataset is usually a tedious
process with manual annotations, (2) most datasets are only designed for a
single specific task, (3) the modification or randomization of the 3D scene is
difficult, and (4) the release of commercial 3D data may encounter copyright
issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl
Synthesis system, to facilitate the 3D scene modification and the 2D image
synthesis for various vision tasks. In particular, we design a programmable
pipeline with Domain-Specific Language, allowing users to (1) select scenes
from the commercial indoor scene database, (2) synthesize scenes for different
tasks with customized rules, and (3) render various imagery data, such as
visual color, geometric structures, semantic label. Our system eases the
difficulty of customizing massive numbers of scenes for different tasks and
relieves users from manipulating fine-grained scene configurations by providing
user-controllable randomness using multi-level samplers. Most importantly, it
empowers users to access commercial scene databases with millions of indoor
scenes and protects the copyright of core data assets, e.g., 3D CAD models. We
demonstrate the validity and flexibility of our system by using our synthesized
data to improve the performance on different kinds of computer vision tasks.
