# Visual Place Representation and Recognition from Depth Images

arXiv: https://arxiv.org/abs/2112.13707 · HTML: https://mlsanity.com/paper/2112.13707/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 76 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [Augmenting Visual Place Recognition with Structural Cues](https://mlsanity.com/paper/2003.00278/) (2020)
- [Learning Effective RGB-D Representations for Scene Recognition](https://mlsanity.com/paper/1809.06269/) (2018)
- [Visual Localization Using Semantic Segmentation and Depth Prediction](https://mlsanity.com/paper/2005.11922/) (2020)
- [Real-Time Visual Place Recognition for Personal Localization on a Mobile Device](https://mlsanity.com/paper/1611.02061/) (2016)
- [Complete 3D Scene Parsing from an RGBD Image](https://mlsanity.com/paper/1710.09490/) (2017)

## Built on by (most cited first, 2 of 2)

- [Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks](https://mlsanity.com/paper/2306.16357/) (2023)
- [Object Recognition System on a Tactile Device for Visually Impaired](https://mlsanity.com/paper/2307.02211/) (2023)

## Abstract (arXiv)

This work proposes a new method for place recognition based on the scene architecture. From depth video, we compute the 3D model and we derive and describe geometrically the 2D map from which the scene descriptor is deduced to constitute the core of the proposed algorithm. The obtained results show the efficiency and the robustness of the propounded descriptor to scene appearance changes and light variations.