# Indoor Scene Recognition in 3D

arXiv: https://arxiv.org/abs/2002.12819 · HTML: https://mlsanity.com/paper/2002.12819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition](https://mlsanity.com/paper/2202.02070/) (2022)
- [Real-time 3D semantic occupancy prediction for autonomous vehicles using memory-efficient sparse convolution](https://mlsanity.com/paper/2403.08748/) (2024)
- [Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/2212.02854/) (2022)
- [Semantic Layering in Room Segmentation via LLMs](https://mlsanity.com/paper/2403.12920/) (2024)
- [TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances](https://mlsanity.com/paper/2412.05596/) (2024)

## Abstract (arXiv)

Recognising in what type of environment one is located is an important perception task. For instance, for a robot operating in indoors it is helpful to be aware whether it is in a kitchen, a hallway or a bedroom. Existing approaches attempt to classify the scene based on 2D images or 2.5D range images. Here, we study scene recognition from 3D point cloud (or voxel) data, and show that it greatly outperforms methods based on 2D birds-eye views. Moreover, we advocate multi-task learning as a way of improving scene recognition, building on the fact that the scene type is highly correlated with the objects in the scene, and therefore with its semantic segmentation into different object classes. In a series of ablation studies, we show that successful scene recognition is not just the recognition of individual objects unique to some scene type (such as a bathtub), but depends on several different cues, including coarse 3D geometry, colour, and the (implicit) distribution of object categories. Moreover, we demonstrate that surprisingly sparse 3D data is sufficient to classify indoor scenes with good accuracy.