  Being data-driven is one of the most iconic properties of deep learning
algorithms. The birth of ImageNet drives a remarkable trend of "learning from
large-scale data" in computer vision. Pretraining on ImageNet to obtain rich
universal representations has been manifested to benefit various 2D visual
tasks, and becomes a standard in 2D vision. However, due to the laborious
collection of real-world 3D data, there is yet no generic dataset serving as a
counterpart of ImageNet in 3D vision, thus how such a dataset can impact the 3D
community is unraveled. To remedy this defect, we introduce MVImgNet, a
large-scale dataset of multi-view images, which is highly convenient to gain by
shooting videos of real-world objects in human daily life. It contains 6.5
million frames from 219,188 videos crossing objects from 238 classes, with rich
annotations of object masks, camera parameters, and point clouds. The
multi-view attribute endows our dataset with 3D-aware signals, making it a soft
bridge between 2D and 3D vision.
  We conduct pilot studies for probing the potential of MVImgNet on a variety
of 3D and 2D visual tasks, including radiance field reconstruction, multi-view
stereo, and view-consistent image understanding, where MVImgNet demonstrates
promising performance, remaining lots of possibilities for future explorations.
  Besides, via dense reconstruction on MVImgNet, a 3D object point cloud
dataset is derived, called MVPNet, covering 87,200 samples from 150 categories,
with the class label on each point cloud. Experiments show that MVPNet can
benefit the real-world 3D object classification while posing new challenges to
point cloud understanding.
  MVImgNet and MVPNet will be publicly available, hoping to inspire the broader
vision community.
