  Our method studies the complex task of object-centric 3D understanding from a
single RGB-D observation. As it is an ill-posed problem, existing methods
suffer from low performance for both 3D shape and 6D pose and size estimation
in complex multi-object scenarios with occlusions. We present ShAPO, a method
for joint multi-object detection, 3D textured reconstruction, 6D object pose
and size estimation. Key to ShAPO is a single-shot pipeline to regress shape,
appearance and pose latent codes along with the masks of each object instance,
which is then further refined in a sparse-to-dense fashion. A novel
disentangled shape and appearance database of priors is first learned to embed
objects in their respective shape and appearance space. We also propose a
novel, octree-based differentiable optimization step, allowing us to further
improve object shape, pose and appearance simultaneously under the learned
latent space, in an analysis-by-synthesis fashion. Our novel joint implicit
textured object representation allows us to accurately identify and reconstruct
novel unseen objects without having access to their 3D meshes. Through
extensive experiments, we show that our method, trained on simulated indoor
scenes, accurately regresses the shape, appearance and pose of novel objects in
the real-world with minimal fine-tuning. Our method significantly out-performs
all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D
pose estimation. Project page:
https://zubair-irshad.github.io/projects/ShAPO.html
