# Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

arXiv: https://arxiv.org/abs/1812.06677 · HTML: https://mlsanity.com/paper/1812.06677/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries](https://mlsanity.com/paper/2211.15658/) (2022)
- [Plan2Scene: Converting Floorplans to 3D Scenes](https://mlsanity.com/paper/2106.05375/) (2021)
- [PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving](https://mlsanity.com/paper/2211.13785/) (2022)
- [PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation](https://mlsanity.com/paper/2203.15965/) (2022)
- [360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation](https://mlsanity.com/paper/2112.06180/) (2021)
- [UnLoc: Leveraging Depth Uncertainties for Floorplan Localization](https://mlsanity.com/paper/2509.11301/) (2025)
- [RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion](https://mlsanity.com/paper/2306.03584/) (2023)
- [Floorplan Restoration by Structure Hallucinating Transformer Cascades](https://mlsanity.com/paper/2206.00645/) (2022)
- [SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas](https://mlsanity.com/paper/2406.19390/) (2024)
- [Structure-preserving Planar Simplification for Indoor Environments](https://mlsanity.com/paper/2408.06814/) (2024)
- [GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization](https://mlsanity.com/paper/2609.08385/) (2026)

## Abstract (arXiv)

We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirement of feature matching, the proposed method enables some novel applications ranging from large or featureless scene reconstruction and modeling from sparse input. We validate our method quantitatively and qualitatively on real and synthetic scenes of various sizes and complexities. The evaluations and comparisons show superior effectiveness and accuracy of our method.