  Recognizing scenes and objects in 3D from a single image is a longstanding
goal of computer vision with applications in robotics and AR/VR. For 2D
recognition, large datasets and scalable solutions have led to unprecedented
advances. In 3D, existing benchmarks are small in size and approaches
specialize in few object categories and specific domains, e.g. urban driving
scenes. Motivated by the success of 2D recognition, we revisit the task of 3D
object detection by introducing a large benchmark, called Omni3D. Omni3D
re-purposes and combines existing datasets resulting in 234k images annotated
with more than 3 million instances and 98 categories. 3D detection at such
scale is challenging due to variations in camera intrinsics and the rich
diversity of scene and object types. We propose a model, called Cube R-CNN,
designed to generalize across camera and scene types with a unified approach.
We show that Cube R-CNN outperforms prior works on the larger Omni3D and
existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D
object recognition and show that it improves single-dataset performance and can
accelerate learning on new smaller datasets via pre-training.
