Object Scene Representation Transformer
This work addresses the problem of improving labeled data efficiency in neural networks for object-centric scene understanding, representing a strong specific gain in the domain of computer vision and neural scene representation.
The paper tackles unsupervised learning of 3D-consistent object decompositions in complex scenes, achieving multiple orders of magnitude faster compositional rendering and scaling to more diverse scenes than existing methods.
A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition. Facilitating the learning of such a representation in neural networks holds promise for substantially improving labeled data efficiency. As a key step in this direction, we make progress on the problem of learning 3D-consistent decompositions of complex scenes into individual objects in an unsupervised fashion. We introduce Object Scene Representation Transformer (OSRT), a 3D-centric model in which individual object representations naturally emerge through novel view synthesis. OSRT scales to significantly more complex scenes with larger diversity of objects and backgrounds than existing methods. At the same time, it is multiple orders of magnitude faster at compositional rendering thanks to its light field parametrization and the novel Slot Mixer decoder. We believe this work will not only accelerate future architecture exploration and scaling efforts, but it will also serve as a useful tool for both object-centric as well as neural scene representation learning communities.