  We introduce Physically Enhanced Gaussian Splatting Simulation System
(PEGASUS) for 6DOF object pose dataset generation, a versatile dataset
generator based on 3D Gaussian Splatting.
  Environment and object representations can be easily obtained using commodity
cameras to reconstruct with Gaussian Splatting. <i>PEGASUS</i> allows the
composition of new scenes by merging the respective underlying Gaussian
Splatting point cloud of an environment with one or multiple objects.
Leveraging a physics engine enables the simulation of natural object placement
within a scene through interaction between meshes extracted for the objects and
the environment. Consequently, an extensive amount of new scenes - static or
dynamic - can be created by combining different environments and objects. By
rendering scenes from various perspectives, diverse data points such as RGB
images, depth maps, semantic masks, and 6DoF object poses can be extracted.
  Our study demonstrates that training on data generated by PEGASUS enables
pose estimation networks to successfully transfer from synthetic data to
real-world data. Moreover, we introduce the Ramen dataset, comprising 30
Japanese cup noodle items. This dataset includes spherical scans that captures
images from both object hemisphere and the Gaussian Splatting reconstruction,
making them compatible with PEGASUS.
