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

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

3 Apr 2019arXiv:1904.02199archive 2025-07-28

Cathrin Elich, Francis Engelmann, Theodora Kontogianni, Bastian Leibe

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.

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Tasks

3D Instance Segmentation 3D Semantic Instance Segmentation Clustering Deep Learning General Classification Instance Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Semantic Instance Segmentation	ScanNetV2	3D-BEVIS	mAP@0.50	24.8	#4 of 5	Archive leaderboard	report

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