UniDet3D: Multi-dataset Indoor 3D Object Detection
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 .
66e0122023e5f162e7d16505
Maksim Kolodiazhnyi
claimed_verified
2024-09-10T09:46:08.653Z
665b10fb270e47e678f2ddf1
/avatars/1bc7a9211acf767f7bfca998c24315a0.svg
max
user
maksimko123
66e0122023e5f162e7d16506
Anna Vorontsova
66e0122023e5f162e7d16507
Matvey Skripkin
admin_assigned
2024-09-10T09:41:56.764Z
6626c5d0a329de26e7eb16fa
/avatars/124f389f768fb666efd8b5a9b54c3b3c.svg
Matvey Skripkin
user
barracuda049
66e0122023e5f162e7d16508
Danila Rukhovich
admin_assigned
2024-09-10T09:42:02.349Z
6394434e872e49c02f5934c3
/avatars/d77228347a45868e7984607b18a98c29.svg
Danila Rukhovich
user
filapro
66e0122023e5f162e7d16509
Anton Konushin
admin_assigned
2024-09-10T09:42:07.787Z
63aae7456ed74f08b7b061cb
/avatars/664e8cdebb49935c671fda0d65c0407a.svg
Anton Konushin
user
ktoshik
UniDet3D: Multi-dataset Indoor 3D Object Detection
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 .
A 3D object detection model trained on a combined indoor dataset achieves superior performance across multiple benchmarks using a unified label space and transformer encoder architecture.
3D object detection point clouds indoor datasets foundation models supervised training label spaces transformer encoder mAP50 mAP25 ScanNet ARKitScenes S3DIS MultiScan 3RScan ScanNet++