# CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition

arXiv: https://arxiv.org/abs/2202.02070 · HTML: https://mlsanity.com/paper/2202.02070/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [MinkLoc3D: Point Cloud Based Large-Scale Place Recognition](https://mlsanity.com/paper/2011.04530/) (2020)
- [Semantic Visual Localization](https://mlsanity.com/paper/1712.05773/) (2017)
- [DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization](https://mlsanity.com/paper/2007.09217/) (2020)
- [A Cross-Season Correspondence Dataset for Robust Semantic Segmentation](https://mlsanity.com/paper/1903.06916/) (2019)
- [Translate-to-Recognize Networks for RGB-D Scene Recognition](https://mlsanity.com/paper/1904.12254/) (2019)
- [ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition](https://mlsanity.com/paper/2110.07703/) (2021)
- [VLASE: Vehicle Localization by Aggregating Semantic Edges](https://mlsanity.com/paper/1807.02536/) (2018)
- [Learning Effective RGB-D Representations for Scene Recognition](https://mlsanity.com/paper/1809.06269/) (2018)
- [LCD -- Line Clustering and Description for Place Recognition](https://mlsanity.com/paper/2010.10867/) (2020)
- [Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation](https://mlsanity.com/paper/2108.02522/) (2021)
- [Indoor Scene Recognition in 3D](https://mlsanity.com/paper/2002.12819/) (2020)

## Built on by (most cited first, 9 of 9)

- [iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking](https://mlsanity.com/paper/2209.07919/) (2022)
- [MSSPlace: Multi-Sensor Place Recognition with Visual and Text Semantics](https://mlsanity.com/paper/2407.15663/) (2024)
- [AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition](https://mlsanity.com/paper/2312.09538/) (2023)
- [PoCo: Point Context Cluster for RGBD Indoor Place Recognition](https://mlsanity.com/paper/2404.02885/) (2024)
- [VIPeR: Visual Incremental Place Recognition with Adaptive Mining and Continual Learning](https://mlsanity.com/paper/2407.21416/) (2024)
- [CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition](https://mlsanity.com/paper/2407.17457/) (2024)
- [DC-VLAQ: Query-Residual Aggregation for Robust Visual Place Recognition](https://mlsanity.com/paper/2601.12729/) (2026)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition](https://mlsanity.com/paper/2605.09418/) (2026)

## Abstract (arXiv)

We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features. It uses a 2-stage deep learning framework, in which the first stage is trained for the auxiliary task of semantic segmentation and the second stage uses features from layers in the first stage to generate discriminate descriptors for place recognition. The auxiliary task encourages the features to be semantically meaningful, hence aggregating the geometry and colour in the RGB point cloud data with implicit semantic information. We use an indoor place recognition dataset derived from the ScanNet dataset for training and evaluation, with a test set comprising 3,608 point clouds generated from 100 different rooms. Comparison with a traditional feature-based method and four state-of-the-art deep learning methods demonstrate that our approach significantly outperforms all five methods, achieving, for example, a top-3 average recall rate of 75% compared with 41% for the closest rival method. Our code is available at: https://github.com/YuhangMing/Semantic-Indoor-Place-Recognition