Seeing 3D Objects in a Single Image
via Self-Supervised Static-Dynamic Disentanglement

Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov, Rares Ambrus, Adrien Gaidon,
William T. Freeman, Fredo Durand Joshua B. Tenenbaum, Vincent Sitzmann

Code (Coming soon)

Human perception reliably identifies movable and immovable parts of 3D scenes, and completes the 3D structure of objects and background from incomplete observations. We learn this skill not via labeled examples, but simply by observing objects move. In this work, we propose an approach that observes unlabeled multi-view videos at training time and learns to map a single image observation of a complex scene, such as a street with cars, to a 3D neural scene representation that is disentangled into movable and immovable parts while plausibly completing its 3D structure. We separately parameterize movable and immovable scene parts via 2D neural ground plans. These ground plans are 2D grids of features aligned with the ground plane that can be locally decoded into 3D neural radiance fields. Our model is trained self-supervised via neural rendering. We demonstrate that the structure inherent to our disentangled 3D representation enables a variety of downstream tasks in street-scale 3D scenes using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance segmentation, and 3D bounding box prediction, highlighting its value as a backbone for data-efficient 3D scene understanding models. This disentanglement further enables scene editing via object manipulation such as deletion, insertion, and rigid-body motion.

Static-Dynamic Disentanglemnt and Novel View Synthesis

Given a single image as input, our method can disentangle the scene into static and dynamic elements of the scene based on objects observed as moving during training. The method generates independent neural groundplans for the static and dynamic components of the scene which can be composed together to render the scene.


Since the method computes independent groundplans for the static and dynamic components, the densities expressed by the dynamic groundplan enable segmentation in the bird's eye-view, instance level segmentation, and bounding box prediction in an unsupervised setup.

Object-Centric Representation and Scene Editing

The neural representations for each object instance identified in a given input is an object-centric representation. These representations can be individually manipulated to edit the scene, enabling object deletion, insertion, and rearrangement.

Comparison for Novel View Synthesis