  Vehicle-to-everything (V2X) is a popular topic in the field of Autonomous
Driving in recent years. Vehicle-infrastructure cooperation (VIC) becomes one
of the important research area. Due to the complexity of traffic conditions
such as blind spots and occlusion, it greatly limits the perception
capabilities of single-view roadside sensing systems. To further enhance the
accuracy of roadside perception and provide better information to the vehicle
side, in this paper, we constructed holographic intersections with various
layouts to build a large-scale multi-sensor holographic vehicle-infrastructure
cooperation dataset, called HoloVIC. Our dataset includes 3 different types of
sensors (Camera, Lidar, Fisheye) and employs 4 sensor-layouts based on the
different intersections. Each intersection is equipped with 6-18 sensors to
capture synchronous data. While autonomous vehicles pass through these
intersections for collecting VIC data. HoloVIC contains in total on 100k+
synchronous frames from different sensors. Additionally, we annotated 3D
bounding boxes based on Camera, Fisheye, and Lidar. We also associate the IDs
of the same objects across different devices and consecutive frames in
sequence. Based on HoloVIC, we formulated four tasks to facilitate the
development of related research. We also provide benchmarks for these tasks.
