UniDet3D: Multi-dataset Indoor 3D Object Detection
Authors: name: Maksim Kolodiazhnyi, name: Anna Vorontsova, name: Matvey Skripkin, name: Danila Rukhovich, name: Anton Konushin

Growing customer demand for smart solutions in robotics and augmented reality
has attracted considerable attention to 3D object detection from point clouds.
Yet, existing indoor datasets taken individually are too small and
insufficiently diverse to train a powerful and general 3D object detection
model. In the meantime, more general approaches utilizing foundation models are
still inferior in quality to those based on supervised training for a specific
task. In this work, we propose , a simple yet effective 3D object
detection model, which is trained on a mixture of indoor datasets and is
capable of working in various indoor environments. By unifying different label
spaces,  enables learning a strong representation across multiple
datasets through a supervised joint training scheme. The proposed network
architecture is built upon a vanilla transformer encoder, making it easy to
run, customize and extend the prediction pipeline for practical use. Extensive
experiments demonstrate that  obtains significant gains over existing 3D
object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50),
ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan
(+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at
https://github.com/filapro/unidet3d .