  Point scene understanding is a challenging task to process real-world scene
point cloud, which aims at segmenting each object, estimating its pose, and
reconstructing its mesh simultaneously. Recent state-of-the-art method first
segments each object and then processes them independently with multiple stages
for the different sub-tasks. This leads to a complex pipeline to optimize and
makes it hard to leverage the relationship constraints between multiple
objects. In this work, we propose a novel Disentangled Object-Centric
TRansformer (DOCTR) that explores object-centric representation to facilitate
learning with multiple objects for the multiple sub-tasks in a unified manner.
Each object is represented as a query, and a Transformer decoder is adapted to
iteratively optimize all the queries involving their relationship. In
particular, we introduce a semantic-geometry disentangled query (SGDQ) design
that enables the query features to attend separately to semantic information
and geometric information relevant to the corresponding sub-tasks. A hybrid
bipartite matching module is employed to well use the supervisions from all the
sub-tasks during training. Qualitative and quantitative experimental results
demonstrate that our method achieves state-of-the-art performance on the
challenging ScanNet dataset. Code is available at
https://github.com/SAITPublic/DOCTR.
