# 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

- **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
- **Year:** 2026
- **Status:** Accepted at ICRA 2026
- **Venue:** ICRA 2026
- **Conference ranking:** A*-ranked conference
- **Research area:** 3D Intelligence
- **First arXiv submission:** 2025-09-23

## 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 mAP@0.25 of 44.0, and mAP@0.5 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 mAP@0.25 / mAP@0.5 | 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 mAP@0.25 / mAP@0.5 | 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 mAP@0.25 / mAP@0.5 | 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 F1@0.25/F1@0.5; detection F1@0.25/F1@0.5 | 90.5/89.6; 73.9/65.4 | TUN3D paper, Table III |

Metrics are reported exactly for the stated input and evaluation setting; rows from different settings should not be treated as directly interchangeable.

## Technologies and methods

- Sparse convolutional networks
- DUSt3R
- TSDF fusion
- Finite-dimensional parametric wall models

## Related M:3L research

- [3D Intelligence](https://m3l.am/research/3d-intelligence.md)
- [CAD Intelligence](https://m3l.am/research/cad-intelligence.md)
- [Agentic AI](https://m3l.am/research/agentic-ai.md)

## External resources

- [arXiv record](https://arxiv.org/abs/2509.21388)
- [DOI](https://doi.org/10.48550/arXiv.2509.21388)
- [Code repository](https://github.com/col14m/tun3d)
- [Project page](https://bulatko.github.io/tun3d/)

## Evidence boundary

Benchmark claims and numerical values above are attributed to the associated paper and its stated protocol. Potential applications are not evidence of completed deployments.

## About M:3L

M:3L is the Mathematical Modeling & Machine Learning Laboratory, a research and engineering laboratory in Yerevan, Armenia, established at the Institute of Mechanics of the National Academy of Sciences of the Republic of Armenia.

## Authoritative URLs

- [Canonical publication page](https://m3l.am/publications/tun3d)
- [All M:3L publications](https://m3l.am/publications.md)
- [M:3L research](https://m3l.am/research.md)