# Improved Block Merging for 3D Point Cloud Instance Segmentation

arXiv: https://arxiv.org/abs/2407.06991 · HTML: https://mlsanity.com/paper/2407.06991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [OccuSeg: Occupancy-aware 3D Instance Segmentation](https://mlsanity.com/paper/2003.06537/) (2020)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [Associatively Segmenting Instances and Semantics in Point Clouds](https://mlsanity.com/paper/1902.09852/) (2019)
- [DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution](https://mlsanity.com/paper/2011.13328/) (2020)
- [JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields](https://mlsanity.com/paper/1904.00699/) (2019)
- [Point Cloud Instance Segmentation using Probabilistic Embeddings](https://mlsanity.com/paper/1912.00145/) (2019)
- [JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1912.09654/) (2019)
- [Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation](https://mlsanity.com/paper/2001.01349/) (2020)
- [3D-BEVIS: Bird's-Eye-View Instance Segmentation](https://mlsanity.com/paper/1904.02199/) (2019)

## Built on by (most cited first, 2 of 2)

- [ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds](https://mlsanity.com/paper/2506.16991/) (2025)
- [ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method](https://mlsanity.com/paper/2410.02352/) (2024)

## Abstract (arXiv)

This paper proposes a novel block merging algorithm suitable for any block-based 3D instance segmentation technique. The proposed work improves over the state-of-the-art by allowing wrongly labelled points of already processed blocks to be corrected through label propagation. By doing so, instance overlap between blocks is not anymore necessary to produce the desirable results, which is the main limitation of the current art. Our experiments show that the proposed block merging algorithm significantly and consistently improves the obtained accuracy for all evaluation metrics employed in literature, regardless of the underlying network architecture.