Learning how to model complex scenes in a modular way with recombinable
components is a pre-requisite for higher-order reasoning and acting in the
physical world. However, current generative models lack the ability to capture
the inherently compositional and layered nature of visual scenes. While recent
work has made progress towards unsupervised learning of object-based scene
representations, most models still maintain a global representation space
(i.e., objects are not explicitly separated), and cannot generate scenes with
novel object arrangement and depth ordering. Here, we present an alternative
approach which uses an inductive bias encouraging modularity by training an
ensemble of generative models (experts). During training, experts compete for
explaining parts of a scene, and thus specialise on different object classes,
with objects being identified as parts that re-occur across multiple scenes.
Our model allows for controllable sampling of individual objects and
recombination of experts in physically plausible ways. In contrast to other
methods, depth layering and occlusion are handled correctly, moving this
approach closer to a causal generative scene model. Experiments on simple toy
data qualitatively demonstrate the conceptual advantages of the proposed
approach.

通过训练专家生成模型的集合，鼓励系统具有模块化特征的认知偏差，从而达到控制采样和可重组性的目的，并且在处理深度分层和遮挡方面具有优势。

通过专家竞争实现因果生成场景模型

Towards causal generative scene models via competition of experts

Robots and other smart devices need efficient object-based scene
representations from their on-board vision systems to reason about contact,
physics and occlusion. Recognized precise object models will play an important
role alongside non-parametric reconstructions of unrecognized structures. We
present a system which can estimate the accurate poses of multiple known
objects in contact and occlusion from real-time, embodied multi-view vision.
Our approach makes 3D object pose proposals from single RGB-D views,
accumulates pose estimates and non-parametric occupancy information from
multiple views as the camera moves, and performs joint optimization to estimate
consistent, non-intersecting poses for multiple objects in contact.
We verify the accuracy and robustness of our approach experimentally on 2
object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We
demonstrate a real-time robotics application where a robot arm precisely and
orderly disassembles complicated piles of objects, using only on-board RGB-D
vision.

提出了一种基于多视角视野的实时视觉系统，可以从单个 RGB-D 视角提出三维物体姿态建议，并且根据多个视角的姿态估计和非参数占用信息积累这些姿态估计，从而对接触的多个已知对象进行一致的、不相交的姿态估计。应用于实时机器人应用程序中，只使用机载 RGB-D 视觉，机械臂可以精确而有序地拆卸复杂的物体堆放，展示了其准确性和鲁棒性。