  We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized
Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box
annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object
classes and 200 times more instances than existing NOCS datasets (NOCS-Real275,
Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS
prediction model (NOCSformer) that can predict accurate NOCS, instance masks
and poses from 2D object detections across diverse classes. It is the first
NOCS model that can generalize to a broad range of classes when prompted with
2D boxes. We evaluate our model on the task of 3D oriented bounding box
prediction, where it achieves comparable results to state-of-the-art 3D
detection methods such as Cube R-CNN. Unlike other 3D detection methods, our
model also provides detailed and accurate 3D object shape and segmentation. We
propose a novel benchmark for the task of NOCS prediction based on OmniNOCS,
which we hope will serve as a useful baseline for future work in this area. Our
dataset and code will be at the project website: https://omninocs.github.io.
