# UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes

arXiv: https://arxiv.org/abs/2303.14541 · HTML: https://mlsanity.com/paper/2303.14541/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 70 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 30 of 42)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding](https://mlsanity.com/paper/2007.10985/) (2020)
- [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021)
- [ConceptFusion: Open-set Multimodal 3D Mapping](https://mlsanity.com/paper/2302.07241/) (2023)
- [Language-Grounded Indoor 3D Semantic Segmentation in the Wild](https://mlsanity.com/paper/2204.07761/) (2022)
- [3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans](https://mlsanity.com/paper/1812.07003/) (2018)
- [Self-Supervised Pretraining of 3D Features on any Point-Cloud](https://mlsanity.com/paper/2101.02691/) (2021)
- [Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts](https://mlsanity.com/paper/2012.09165/) (2020)
- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [Cut and Learn for Unsupervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2301.11320/) (2023)
- [OccuSeg: Occupancy-aware 3D Instance Segmentation](https://mlsanity.com/paper/2003.06537/) (2020)
- [PLA: Language-Driven Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2211.16312/) (2022)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut](https://mlsanity.com/paper/2202.11539/) (2022)
- [CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory](https://mlsanity.com/paper/2210.05663/) (2022)
- [Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/1906.01140/) (2019)
- [GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud](https://mlsanity.com/paper/1812.03320/) (2018)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation](https://mlsanity.com/paper/1803.10409/) (2018)
- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)
- [Superpoint Transformer for 3D Scene Instance Segmentation](https://mlsanity.com/paper/2211.15766/) (2022)

## Built on by (most cited first, 30 of 31)

- [SAI3D: Segment Any Instance in 3D Scenes](https://mlsanity.com/paper/2312.11557/) (2023)
- [OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation](https://mlsanity.com/paper/2309.00616/) (2023)
- [SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2311.17707/) (2023)
- [EmbodiedSAM: Online Segment Any 3D Thing in Real Time](https://mlsanity.com/paper/2408.11811/) (2024)
- [Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding](https://mlsanity.com/paper/2308.00353/) (2023)
- [SAM-guided Graph Cut for 3D Instance Segmentation](https://mlsanity.com/paper/2312.08372/) (2023)
- [Spatial Understanding from Videos: Structured Prompts Meet Simulation Data](https://mlsanity.com/paper/2506.03642/) (2025)
- [Part2Object: Hierarchical Unsupervised 3D Instance Segmentation](https://mlsanity.com/paper/2407.10084/) (2024)
- [Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description](https://mlsanity.com/paper/2412.01398/) (2024)
- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)
- [Pointmap Association and Piecewise-Plane Constraint for Consistent and Compact 3D Gaussian Segmentation Field](https://mlsanity.com/paper/2502.16303/) (2025)
- [FreePoint: Unsupervised Point Cloud Instance Segmentation](https://mlsanity.com/paper/2305.06973/) (2023)
- [UNIT: Unsupervised Online Instance Segmentation through Time](https://mlsanity.com/paper/2409.07887/) (2024)
- [DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting](https://mlsanity.com/paper/2412.10972/) (2024)
- [Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding](https://mlsanity.com/paper/2504.06719/) (2025)
- [Sketchy Bounding-box Supervision for 3D Instance Segmentation](https://mlsanity.com/paper/2505.16399/) (2025)
- [MoonSeg3R: Monocular Online Zero-Shot Segment Anything in 3D with Reconstructive Foundation Priors](https://mlsanity.com/paper/2512.15577/) (2025)
- [AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans](https://mlsanity.com/paper/2403.16318/) (2024)
- [Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation](https://mlsanity.com/paper/2504.11024/) (2025)
- [GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision](https://mlsanity.com/paper/2504.11754/) (2025)
- [SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing](https://mlsanity.com/paper/2509.05144/) (2025)
- [Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment](https://mlsanity.com/paper/2312.00663/) (2023)
- [Semantic UV mapping to improve texture inpainting for indoor scenes](https://mlsanity.com/paper/2407.09248/) (2024)
- [Hierarchical Image-Guided 3D Point Cloud Segmentation in Industrial Scenes via Multi-View Bayesian Fusion](https://mlsanity.com/paper/2512.06882/) (2025)
- [ESAM++: Efficient Online 3D Perception on the Edge](https://mlsanity.com/paper/2605.29505/) (2026)
- [SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video](https://mlsanity.com/paper/2607.01784/) (2026)
- [Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss](https://mlsanity.com/paper/2509.23194/) (2025)
- [Class-agnostic 3D Segmentation by Granularity-Consistent Automatic 2D Mask Tracking](https://mlsanity.com/paper/2511.00785/) (2025)
- [FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation](https://mlsanity.com/paper/2605.27178/) (2026)
- [SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring](https://mlsanity.com/paper/2606.29376/) (2026)

## Abstract (arXiv)

3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive, manual 3D annotations. We propose UnScene3D, the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of geometric oversegmentation, enabling efficient representation and learning on high-resolution 3D data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over state-of-the-art unsupervised 3D instance segmentation methods by more than 300% Average Precision score, demonstrating effective instance segmentation even in challenging, cluttered 3D scenes.