InstanceBEV: Unifying Instance and BEV Representation for Global Modeling

• URL: http://arxiv.org/abs/2505.13817v1
• Date: Tue, 20 May 2025 01:56:50 GMT
• Title: InstanceBEV: Unifying Instance and BEV Representation for Global Modeling
• Authors: Feng Li, Kun Xu, Zhaoyue Wang, Yunduan Cui, Mohammad Masum Billah, Jia Liu,
• Abstract summary: Occupancy Grid Maps are widely used in navigation for their ability to represent 3D space occupancy.<n>Bird's-Eye View (BEV) perspective offers a more practical solution for autonomous driving.<n>We propose InstanceBEV, the first method to introduce instance-level dimensionality reduction for BEV.
• Score: 12.046822591946423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Occupancy Grid Maps are widely used in navigation for their ability to represent 3D space occupancy. However, existing methods that utilize multi-view cameras to construct Occupancy Networks for perception modeling suffer from cubic growth in data complexity. Adopting a Bird's-Eye View (BEV) perspective offers a more practical solution for autonomous driving, as it provides higher semantic density and mitigates complex object occlusions. Nonetheless, BEV-based approaches still require extensive engineering optimizations to enable efficient large-scale global modeling. To address this challenge, we propose InstanceBEV, the first method to introduce instance-level dimensionality reduction for BEV, enabling global modeling with transformers without relying on sparsification or acceleration operators. Different from other BEV methods, our approach directly employs transformers to aggregate global features. Compared to 3D object detection models, our method samples global feature maps into 3D space. Experiments on OpenOcc-NuScenes dataset show that InstanceBEV achieves state-of-the-art performance while maintaining a simple, efficient framework without requiring additional optimizations.

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