ICRA 2026
TUN3D: Towards Real-World Scene Understanding from Unposed Images
The first method for joint layout estimation and 3D detection without camera poses or depth supervision, designed to work with point clouds, posed images, or unposed RGB captures.
Publication record
- Status
- Accepted at ICRA 2026
- Authors
- Anton Konushin, Nikita Drozdov, Bulat Gabdullin, Alexey Zakharov, Anna Vorontsova, Danila Rukhovich, Maksim Kolodiazhnyi
- Venue
- ICRA 2026
- Research area
- 3D Intelligence
- First arXiv submission
Abstract / summary
TUN3D jointly estimates indoor scene layouts and detects 3D objects from point clouds, posed images, or fully unposed multi-view RGB. In the unposed setting it does not require known camera poses, camera calibration, or externally provided depth as input.
Methodology
- Lightweight sparse-convolutional scene backbone
- Dedicated 3D object-detection and wall-layout heads
- A bird's-eye-view parametric wall representation with two 2D corner offsets and wall height
- DUSt3R reconstruction for posed or fully unposed image collections
- Joint optimization of object classification, 3D boxes, wall classification, and wall parameters
Datasets and benchmarks
- ScanNet
- S3DIS
- ARKitScenes
- Structured3D
Principal results
- The paper presents the first reported joint layout-estimation and 3D-detection results on real scans from fully unposed images.
- On unposed ScanNet imagery, TUN3D reports layout F1 of 46.5, object-detection [email protected] of 44.0, and [email protected] of 20.7.
- With ground-truth point clouds, the paper reports state-of-the-art layout results across ScanNet, S3DIS, ARKitScenes, and Structured3D at publication time.
Benchmark evidence
| Benchmark | Task | Input | Metric | Result | Evidence |
|---|---|---|---|---|---|
| ScanNet | Joint layout estimation and 3D object detection | Unposed multi-view RGB; no known poses or external depth | Layout F1 / detection [email protected] / [email protected] | 46.5 / 44.0 / 20.7 | TUN3D paper, Table I |
| S3DIS | Joint layout estimation and 3D object detection | Unposed multi-view RGB; no known poses or external depth | Layout F1 / detection [email protected] / [email protected] | 20.8 / 11.0 / 2.2 | TUN3D paper, Table I |
| ScanNet | Joint layout estimation and 3D object detection | Posed multi-view RGB; no ground-truth depth | Layout F1 / detection [email protected] / [email protected] | 55.2 / 57.4 / 35.6 | TUN3D paper, Table I |
| ARKitScenes | Layout estimation | Ground-truth point cloud; cross-dataset evaluation | Layout F1 | 30.3 | TUN3D paper, Table II The paper reports 25.9 for Omni-PQ under the same table protocol. |
| Structured3D | Layout estimation and 3D object detection | Ground-truth point cloud | Layout [email protected]/[email protected]; detection [email protected]/[email protected] | 90.5/89.6; 73.9/65.4 | TUN3D paper, Table III |
Values are attributed to the associated paper and the stated input and evaluation protocol.
Technologies and methods
- Sparse convolutional networks
- DUSt3R
- TSDF fusion
- Finite-dimensional parametric wall models
Related M:3L research
Evidence boundary
Benchmark claims and numerical values are paper-reported results for the named protocol. Potential applications are not evidence of completed deployments.