  Discriminative models for object classification typically learn image-based
representations that do not capture the compositional and 3D nature of objects.
In this work, we show that explicitly integrating 3D compositional object
representations into deep networks for image classification leads to a largely
enhanced generalization in out-of-distribution scenarios. In particular, we
introduce a novel architecture, referred to as \OURS, that consists of a
feature extractor and a \textit{neural object volume} for every target object
class. Each neural object volume is a composition of 3D Gaussians that emit
feature vectors. This compositional object representation allows for a highly
robust and fast estimation of the object class by independently matching the
features of the 3D Gaussians of each category to features extracted from an
input image. Additionally, the object pose can be estimated via inverse
rendering of the corresponding neural object volume. To enable the
classification of objects, the neural features at each 3D Gaussian are trained
discriminatively to be distinct from (i) the features of 3D Gaussians in other
categories, (ii) features of other 3D Gaussians of the same object, and (iii)
the background features. Our experiments show that \OURS offers intriguing
advantages over standard architectures due to the 3D compositional structure of
the object representation, namely: (1) An exceptional robustness across a
spectrum of real-world and synthetic out-of-distribution shifts and (2) an
enhanced human interpretability compared to standard models, all while
maintaining real-time inference and a competitive accuracy on in-distribution
data.
