# 3D-BEVIS: Bird's-Eye-View Instance Segmentation

arXiv: https://arxiv.org/abs/1904.02199 · HTML: https://mlsanity.com/paper/1904.02199/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 17 of 17)

- [Mask3D: Mask Transformer for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2210.03105/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining](https://mlsanity.com/paper/2111.15210/) (2021)
- [ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds](https://mlsanity.com/paper/2506.16991/) (2025)
- [Volumetric Semantically Consistent 3D Panoptic Mapping](https://mlsanity.com/paper/2309.14737/) (2023)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage](https://mlsanity.com/paper/2206.13082/) (2022)
- [FreePoint: Unsupervised Point Cloud Instance Segmentation](https://mlsanity.com/paper/2305.06973/) (2023)
- [Dynamic Convolution for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/2107.08392/) (2021)
- [SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests](https://mlsanity.com/paper/2606.08206/) (2026)
- [MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds](https://mlsanity.com/paper/2307.01864/) (2023)
- [Improved Block Merging for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/2407.06991/) (2024)
- [ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method](https://mlsanity.com/paper/2410.02352/) (2024)
- [RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation](https://mlsanity.com/paper/2505.15373/) (2025)
- [Joint prototype and coefficient prediction for 3D instance segmentation](https://mlsanity.com/paper/2407.06958/) (2024)
- [Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes](https://mlsanity.com/paper/2206.01203/) (2022)
- [PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance](https://mlsanity.com/paper/2307.07708/) (2023)

## Abstract (arXiv)

Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.