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

## TL;DR

3D-BEVIS introduces a novel deep learning framework for 3D semantic instance segmentation on point clouds, combining local geometry with global context from bird's-eye view to improve clustering and segmentation accuracy.

## Contribution

The paper proposes a new method that integrates local point features with global bird's-eye view context for more effective 3D instance segmentation.

## Key findings

- Effective clustering of instances in 3D point clouds.
- Improved segmentation accuracy over existing methods.
- Scalable approach combining local and global features.

## Abstract

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.