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Multi-view detection incorporates multiple camera views to alleviateocclusion in crowded scenes, where the state-of-the-art approaches adopthomography transformations to project multi-view features to the ground plane.However, we find that these 2D transformations do not take into account theobject's height, and with this neglection features along the vertical directionof same object are likely not projected onto the same ground plane point,leading to impure ground-plane features. To solve this problem, we propose VFA,voxelized 3D feature aggregation, for feature transformation and aggregation inmulti-view detection. Specifically, we voxelize the 3D space, project thevoxels onto each camera view, and associate 2D features with these projectedvoxels. This allows us to identify and then aggregate 2D features along thesame vertical line, alleviating projection distortions to a large extent.Additionally, because different kinds of objects (human vs. cattle) havedifferent shapes on the ground plane, we introduce the oriented Gaussianencoding to match such shapes, leading to increased accuracy and efficiency. Weperform experiments on multiview 2D detection and multiview 3D detectionproblems. Results on four datasets (including a newly introduced MultiviewCdataset) show that our system is very competitive compared with thestate-of-the-art approaches. %Our code and data will be open-sourced.Code andMultiviewC are released at https://github.com/Robert-Mar/VFA.

Voxelized 3D Feature Aggregation for Multiview Detection