  Understanding and modeling the 3D scene from a single image is a practical
problem. A recent advance proposes a panoptic 3D scene reconstruction task that
performs both 3D reconstruction and 3D panoptic segmentation from a single
image. Although having made substantial progress, recent works only focus on
top-down approaches that fill 2D instances into 3D voxels according to
estimated depth, which hinders their performance by two ambiguities. (1)
instance-channel ambiguity: The variable ids of instances in each scene lead to
ambiguity during filling voxel channels with 2D information, confusing the
following 3D refinement. (2) voxel-reconstruction ambiguity: 2D-to-3D lifting
with estimated single view depth only propagates 2D information onto the
surface of 3D regions, leading to ambiguity during the reconstruction of
regions behind the frontal view surface. In this paper, we propose BUOL, a
Bottom-Up framework with Occupancy-aware Lifting to address the two issues for
panoptic 3D scene reconstruction from a single image. For instance-channel
ambiguity, a bottom-up framework lifts 2D information to 3D voxels based on
deterministic semantic assignments rather than arbitrary instance id
assignments. The 3D voxels are then refined and grouped into 3D instances
according to the predicted 2D instance centers. For voxel-reconstruction
ambiguity, the estimated multi-plane occupancy is leveraged together with depth
to fill the whole regions of things and stuff. Our method shows a tremendous
performance advantage over state-of-the-art methods on synthetic dataset
3D-Front and real-world dataset Matterport3D. Code and models are available in
https://github.com/chtsy/buol.
