# Learning Gaussian Instance Segmentation in Point Clouds

arXiv: https://arxiv.org/abs/2007.09860 · HTML: https://mlsanity.com/paper/2007.09860/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 30 of 33)

- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [Superpoint Transformer for 3D Scene Instance Segmentation](https://mlsanity.com/paper/2211.15766/) (2022)
- [Mask-Attention-Free Transformer for 3D Instance Segmentation](https://mlsanity.com/paper/2309.01692/) (2023)
- [3D Instances as 1D Kernels](https://mlsanity.com/paper/2207.07372/) (2022)
- [MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation](https://mlsanity.com/paper/2203.14662/) (2022)
- [Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation](https://mlsanity.com/paper/2406.02548/) (2024)
- [UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation](https://mlsanity.com/paper/2305.02627/) (2023)
- [Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2201.05972/) (2022)
- [Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining](https://mlsanity.com/paper/2111.15210/) (2021)
- [Scalable SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2209.08263/) (2022)
- [3D-QueryIS: A Query-based Framework for 3D Instance Segmentation](https://mlsanity.com/paper/2211.09375/) (2022)
- [NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2207.09978/) (2022)
- [When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision](https://mlsanity.com/paper/2309.00828/) (2023)
- [Uni3DL: Unified Model for 3D and Language Understanding](https://mlsanity.com/paper/2312.03026/) (2023)
- [BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation](https://mlsanity.com/paper/2403.15019/) (2024)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [Details Matter for Indoor Open-vocabulary 3D Instance Segmentation](https://mlsanity.com/paper/2507.23134/) (2025)
- [3D Indoor Instance Segmentation in an Open-World](https://mlsanity.com/paper/2309.14338/) (2023)
- [MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation](https://mlsanity.com/paper/2411.01781/) (2024)
- [All in One: Visual-Description-Guided Unified Point Cloud Segmentation](https://mlsanity.com/paper/2507.05211/) (2025)
- [PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2101.11987/) (2021)
- [Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey](https://mlsanity.com/paper/2402.12923/) (2024)
- [Beyond the Final Layer: Hierarchical Query Fusion Transformer with Agent-Interpolation Initialization for 3D Instance Segmentation](https://mlsanity.com/paper/2502.04139/) (2025)
- [Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation](https://mlsanity.com/paper/2208.04766/) (2022)
- [Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation](https://mlsanity.com/paper/2406.16776/) (2024)
- [ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method](https://mlsanity.com/paper/2410.02352/) (2024)
- [EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation](https://mlsanity.com/paper/2312.05602/) (2023)
- [Joint prototype and coefficient prediction for 3D instance segmentation](https://mlsanity.com/paper/2407.06958/) (2024)
- [CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation](https://mlsanity.com/paper/2502.17429/) (2025)

## Abstract (arXiv)

This paper presents a novel method for instance segmentation of 3D point clouds. The proposed method is called Gaussian Instance Center Network (GICN), which can approximate the distributions of instance centers scattered in the whole scene as Gaussian center heatmaps. Based on the predicted heatmaps, a small number of center candidates can be easily selected for the subsequent predictions with efficiency, including i) predicting the instance size of each center to decide a range for extracting features, ii) generating bounding boxes for centers, and iii) producing the final instance masks. GICN is a single-stage, anchor-free, and end-to-end architecture that is easy to train and efficient to perform inference. Benefited from the center-dictated mechanism with adaptive instance size selection, our method achieves state-of-the-art performance in the task of 3D instance segmentation on ScanNet and S3DIS datasets.