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What is: Voxel RoI Pooling?

SourceVoxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

Voxel RoI Pooling is a RoI feature extractor extracts RoI features directly from voxel features for further refinement. It starts by dividing a region proposal into G×G×GG \times G \times G regular sub-voxels. The center point is taken as the grid point of the corresponding sub-voxel. Since 3D3 D feature volumes are extremely sparse (non-empty voxels account for <3%<3 \% spaces), we cannot directly utilize max pooling over features of each sub-voxel. Instead, features are integrated from neighboring voxels into the grid points for feature extraction. Specifically, given a grid point g_ig\_{i}, we first exploit voxel query to group a set of neighboring voxels \Gamma\_{i}=\left\(\mathbf{v}\_{i}^{1}, \mathbf{v}\_{i}^{2}, \cdots, \mathbf{v}\_{i}^{K}\right\) . Then, we aggregate the neighboring voxel features with a PointNet module a\mathrm{a} as:

\mathbf{\eta}\_{i}=\max _{k=1,2, \cdots, K}\left\(\Psi\left(\left[\mathbf{v}\_{i}^{k}-\mathbf{g}\_{i} ; \mathbf{\phi}\_{i}^{k}\right]\right)\right\)

where v_ig_i\mathbf{v}\_{i}-\mathbf{g}\_{i} represents the relative coordinates, ϕ_ik\mathbf{\phi}\_{i}^{k} is the voxel feature of v_ik\mathbf{v}\_{i}^{k}, and Ψ()\Psi(\cdot) indicates an MLP. The max pooling operation max()\max (\cdot) is performed along the channels to obtain the aggregated feature vector ηi.\eta_{i} . Particularly, Voxel RoI pooling is exploited to extract voxel features from the 3D feature volumes out of the last two stages in the 3D3 \mathrm{D} backbone network. And for each stage, two Manhattan distance thresholds are set to group voxels with multiple scales. Then, we concatenate the aggregated features pooled from different stages and scales to obtain the RoI features.