# SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation

arXiv: https://arxiv.org/abs/2012.10217 · HTML: https://mlsanity.com/paper/2012.10217/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

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

- [NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo](https://mlsanity.com/paper/2109.01129/) (2021)
- [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds](https://mlsanity.com/paper/2104.04891/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention](https://mlsanity.com/paper/2303.13014/) (2023)
- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://mlsanity.com/paper/2305.19812/) (2023)
- [PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting](https://mlsanity.com/paper/2410.17505/) (2024)
- [LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation](https://mlsanity.com/paper/2211.05997/) (2022)
- [Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining](https://mlsanity.com/paper/2111.15210/) (2021)
- [Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2207.09084/) (2022)
- [Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning](https://mlsanity.com/paper/2205.03137/) (2022)
- [Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation](https://mlsanity.com/paper/2210.05534/) (2022)
- [Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision](https://mlsanity.com/paper/2201.02963/) (2022)
- [PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2202.10705/) (2022)
- [ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations](https://mlsanity.com/paper/2307.09732/) (2023)
- [Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation](https://mlsanity.com/paper/2311.01989/) (2023)
- [Open-Vocabulary SAM3D: Towards Training-free Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2405.15580/) (2024)
- [Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization](https://mlsanity.com/paper/2401.06975/) (2024)
- [Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning](https://mlsanity.com/paper/2212.01261/) (2022)
- [Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation](https://mlsanity.com/paper/2502.04111/) (2025)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Weakly Supervised 3D Instance Segmentation without Instance-level Annotations](https://mlsanity.com/paper/2308.01721/) (2023)
- [Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2410.08091/) (2024)
- [Bayesian Self-Training for Semi-Supervised 3D Segmentation](https://mlsanity.com/paper/2409.08102/) (2024)
- [You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving](https://mlsanity.com/paper/2502.19698/) (2025)
- [Ambiguity-aware Point Cloud Segmentation by Adaptive Margin Contrastive Learning](https://mlsanity.com/paper/2507.06592/) (2025)
- [3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement](https://mlsanity.com/paper/2510.17875/) (2025)
- [You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding](https://mlsanity.com/paper/2303.14727/) (2023)
- [Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels](https://mlsanity.com/paper/2312.06799/) (2023)
- [PointVDP: Learning View-Dependent Projection by Fireworks Rays for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2507.06618/) (2025)
- [Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2508.19909/) (2025)

## Abstract (arXiv)

Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. However, such strong supervision suffers from large annotation costs, arousing the need to study efficient annotating. In this paper, we discover that the locations of instances matter for both instance and semantic 3D scene segmentation. By fully taking advantage of locations, we design a weakly-supervised point cloud segmentation method that only requires clicking on one point per instance to indicate its location for annotation. With over-segmentation for pre-processing, we extend these location annotations into segments as seg-level labels. We further design a segment grouping network (SegGroup) to generate point-level pseudo labels under seg-level labels by hierarchically grouping the unlabeled segments into the relevant nearby labeled segments, so that existing point-level supervised segmentation models can directly consume these pseudo labels for training. Experimental results show that our seg-level supervised method (SegGroup) achieves comparable results with the fully annotated point-level supervised methods. Moreover, it outperforms the recent weakly-supervised methods given a fixed annotation budget. Code is available at https://github.com/AnTao97/SegGroup.