# Real-time Progressive 3D Semantic Segmentation for Indoor Scene

arXiv: https://arxiv.org/abs/1804.00257 · HTML: https://mlsanity.com/paper/1804.00257/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Incremental 3D Semantic Scene Graph Prediction from RGB Sequences](https://mlsanity.com/paper/2305.02743/) (2023)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes](https://mlsanity.com/paper/2108.05246/) (2021)
- [LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices](https://mlsanity.com/paper/2108.03917/) (2021)
- [GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields](https://mlsanity.com/paper/2404.00931/) (2024)
- [PanopticNDT: Efficient and Robust Panoptic Mapping](https://mlsanity.com/paper/2309.13635/) (2023)
- [ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction](https://mlsanity.com/paper/2311.18068/) (2023)
- [TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction](https://mlsanity.com/paper/2105.07468/) (2021)
- [Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans](https://mlsanity.com/paper/2206.02366/) (2022)
- [Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video](https://mlsanity.com/paper/2303.09248/) (2023)
- [SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation](https://mlsanity.com/paper/2306.16585/) (2023)
- [Deep Learning-Based 3D Instance and Semantic Segmentation: A Review](https://mlsanity.com/paper/2406.13308/) (2024)
- [Semantic Dense Reconstruction with Consistent Scene Segments](https://mlsanity.com/paper/2109.14821/) (2021)
- [Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes](https://mlsanity.com/paper/2111.11103/) (2021)
- [Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization](https://mlsanity.com/paper/2307.01121/) (2023)

## Abstract (arXiv)

The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation. In this paper, we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. To guarantee (near) real-time performance, our method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation. We extensively evaluate our method on different indoor scenes including kitchens, offices, and bedrooms in the SceneNN and ScanNet datasets and show that our technique consistently produces state-of-the-art segmentation results in both qualitative and quantitative experiments.