# Monocular Object and Plane SLAM in Structured Environments

arXiv: https://arxiv.org/abs/1809.03415 · HTML: https://mlsanity.com/paper/1809.03415/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 23 of 23)

- [PlaneTR: Structure-Guided Transformers for 3D Plane Recovery](https://mlsanity.com/paper/2107.13108/) (2021)
- [PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos](https://mlsanity.com/paper/2206.07710/) (2022)
- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras](https://mlsanity.com/paper/2207.06058/) (2022)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [Stereo CenterNet based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.11071/) (2021)
- [360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation](https://mlsanity.com/paper/2112.06180/) (2021)
- [TextSLAM: Visual SLAM with Semantic Planar Text Features](https://mlsanity.com/paper/2305.10029/) (2023)
- [CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps](https://mlsanity.com/paper/2402.06092/) (2024)
- [PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN](https://mlsanity.com/paper/2103.15428/) (2021)
- [AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos](https://mlsanity.com/paper/2411.19950/) (2024)
- [S3LAM: Structured Scene SLAM](https://mlsanity.com/paper/2109.07339/) (2021)
- [Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction](https://mlsanity.com/paper/2108.04281/) (2021)
- [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://mlsanity.com/paper/2210.12935/) (2022)
- [Incremental Abstraction in Distributed Probabilistic SLAM Graphs](https://mlsanity.com/paper/2109.06241/) (2021)
- [PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes](https://mlsanity.com/paper/2402.06131/) (2024)
- [Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment](https://mlsanity.com/paper/2110.08977/) (2021)
- [MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization](https://mlsanity.com/paper/2512.03522/) (2025)
- [VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction](https://mlsanity.com/paper/2608.15260/) (2026)
- [Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence](https://mlsanity.com/paper/2104.07077/) (2021)
- [Floorplan-SLAM: A Real-Time, High-Accuracy, and Long-Term Multi-Session Point-Plane SLAM for Efficient Floorplan Reconstruction](https://mlsanity.com/paper/2503.00397/) (2025)
- [Semi-Supervised 360 Layout Estimation with Panoramic Collaborative Perturbations](https://mlsanity.com/paper/2503.01114/) (2025)

## Abstract (arXiv)

In this paper, we present a monocular Simultaneous Localization and Mapping (SLAM) algorithm using high-level object and plane landmarks. The built map is denser, more compact and semantic meaningful compared to feature point based SLAM. We first propose a high order graphical model to jointly infer the 3D object and layout planes from single images considering occlusions and semantic constraints. The extracted objects and planes are further optimized with camera poses in a unified SLAM framework. Objects and planes can provide more semantic constraints such as Manhattan plane and object supporting relationships compared to points. Experiments on various public and collected datasets including ICL NUIM and TUM Mono show that our algorithm can improve camera localization accuracy compared to state-of-the-art SLAM especially when there is no loop closure, and also generate dense maps robustly in many structured environments.