Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy

• URL: http://arxiv.org/abs/2507.21358v4
• Date: Sun, 03 Aug 2025 22:46:43 GMT
• Title: Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy
• Authors: Jicheng Yuan, Manh Nguyen Duc, Qian Liu, Manfred Hauswirth, Danh Le Phuoc,
• Abstract summary: Vision-based bird's-eye-view (BEV) 3D object detection has advanced significantly in autonomous driving.<n>Existing methods often construct 3D BEV representations by collapsing extracted object features.<n>We introduce a multi-task learning framework, Collaborative Perceiver, to bridge gaps in spatial representations and feature refinement.
• Score: 7.570294108494611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-based bird's-eye-view (BEV) 3D object detection has advanced significantly in autonomous driving by offering cost-effectiveness and rich contextual information. However, existing methods often construct BEV representations by collapsing extracted object features, neglecting intrinsic environmental contexts, such as roads and pavements. This hinders detectors from comprehensively perceiving the characteristics of the physical world. To alleviate this, we introduce a multi-task learning framework, Collaborative Perceiver (CoP), that leverages spatial occupancy as auxiliary information to mine consistent structural and conceptual similarities shared between 3D object detection and occupancy prediction tasks, bridging gaps in spatial representations and feature refinement. To this end, we first propose a pipeline to generate dense occupancy ground truths incorporating local density information (LDO) for reconstructing detailed environmental information. Next, we employ a voxel-height-guided sampling (VHS) strategy to distill fine-grained local features according to distinct object properties. Furthermore, we develop a global-local collaborative feature fusion (CFF) module that seamlessly integrates complementary knowledge between both tasks, thus composing more robust BEV representations. Extensive experiments on the nuScenes benchmark demonstrate that CoP outperforms existing vision-based frameworks, achieving 49.5\% mAP and 59.2\% NDS on the test set. Code and supplementary materials are available at this link https://github.com/jichengyuan/Collaborative-Perceiver.

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