  We present NOLBO, a variational observation model estimation for 3D
multi-object from 2D single shot. Previous probabilistic instance-level
understandings mainly consider the single-object image, not single shot with
multi-object; relations between objects and the entire scene are out of their
focus. The objectness of each observation also hardly join their model.
Therefore, we propose a method to approximate the Bayesian observation model of
scene-level 3D multi-object understanding. By exploiting variational
auto-encoder (VAE), we estimate latent variables from the entire scene, which
follow tractable distributions and concurrently imply 3D full shape and pose.
To perform object-oriented data association and probabilistic simultaneous
localization and mapping (SLAM), our observation models can easily be adopted
to probabilistic inference by replacing object-oriented features with latent
variables.

Computer Vision and Pattern Recognition;Robotics