TUN3D: Towards Real-World Scene Understanding from Unposed Images

• URL: http://arxiv.org/abs/2509.21388v1
• Date: Tue, 23 Sep 2025 20:24:07 GMT
• Title: TUN3D: Towards Real-World Scene Understanding from Unposed Images
• Authors: Anton Konushin, Nikita Drozdov, Bulat Gabdullin, Alexey Zakharov, Anna Vorontsova, Danila Rukhovich, Maksim Kolodiazhnyi,
• Abstract summary: TUN3D is a new method that tackles joint layout estimation and 3D object detection in real scans.<n>It does not require ground-truth camera poses or depth supervision.<n>It achieves state-of-the-art performance across three challenging scene understanding benchmarks.
• Score: 11.23080017635425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

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