TUN3D: Towards Real-World Scene Understanding from Unposed Images
Layout estimation and 3D object detection are two fundamental tasks in indoor
scene understanding. When combined, they enable the creation of a compact yet
semantically rich spatial representation of a scene. Existing approaches
typically rely on point cloud input, which poses a major limitation since most
consumer cameras lack depth sensors and visual-only data remains far more
common. We address this issue with TUN3D, the first method that tackles joint
layout estimation and 3D object detection in real scans, given multi-view
images as input, and does not require ground-truth camera poses or depth
supervision. Our approach builds on a lightweight sparse-convolutional backbone
and employs two dedicated heads: one for 3D object detection and one for layout
estimation, leveraging a novel and effective parametric wall representation.
Extensive experiments show that TUN3D achieves state-of-the-art performance
across three challenging scene understanding benchmarks: (i) using ground-truth
point clouds, (ii) using posed images, and (iii) using unposed images. While
performing on par with specialized 3D object detection methods, TUN3D
significantly advances layout estimation, setting a new benchmark in holistic
indoor scene understanding. Code is available at
https://github.com/col14m/tun3d .
68da66cacfa03a5158a1edae
Anton Konushin
68da66cacfa03a5158a1edaf
Nikita Drozdov
claimed_verified
2025-10-01T10:25:07.812Z
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Nikita Drozdov
user
drozdgk
68da66cacfa03a5158a1edb0
Bulat Gabdullin
68da66cacfa03a5158a1edb1
Alexey Zakharov
68da66cacfa03a5158a1edb2
Anna Vorontsova
68da66cacfa03a5158a1edb3
Danila Rukhovich
68da66cacfa03a5158a1edb4
Maksim Kolodiazhnyi
claimed_verified
2025-09-30T06:37:07.718Z
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max
user
maksimko123
TUN3D: Towards Real-World Scene Understanding from Unposed Images
Layout estimation and 3D object detection are two fundamental tasks in indoor
scene understanding. When combined, they enable the creation of a compact yet
semantically rich spatial representation of a scene. Existing approaches
typically rely on point cloud input, which poses a major limitation since most
consumer cameras lack depth sensors and visual-only data remains far more
common. We address this issue with TUN3D, the first method that tackles joint
layout estimation and 3D object detection in real scans, given multi-view
images as input, and does not require ground-truth camera poses or depth
supervision. Our approach builds on a lightweight sparse-convolutional backbone
and employs two dedicated heads: one for 3D object detection and one for layout
estimation, leveraging a novel and effective parametric wall representation.
Extensive experiments show that TUN3D achieves state-of-the-art performance
across three challenging scene understanding benchmarks: (i) using ground-truth
point clouds, (ii) using posed images, and (iii) using unposed images. While
performing on par with specialized 3D object detection methods, TUN3D
significantly advances layout estimation, setting a new benchmark in holistic
indoor scene understanding. Code is available at
https://github.com/col14m/tun3d .
TUN3D is a method for joint layout estimation and 3D object detection using multi-view images without depth sensors or ground-truth camera poses, achieving state-of-the-art performance on indoor scene understanding benchmarks.
sparse-convolutional backbone parametric wall representation 3D object detection layout estimation indoor scene understanding multi-view images