What is Voxel RoI Pooling?
Voxel RoI Pooling is an algorithm in computer vision which extracts region of interest (RoI) features directly from voxel features for further refinement. It is used to detect and classify objects in three-dimensional images or videos by dividing a region proposal into a regular sub-voxel grid. This grid is used to group neighboring voxels and create an aggregated feature vector that is used to identify the RoI features.
How Does Voxel RoI Pooling Work?
The first step in using Voxel RoI Pooling is to divide the region proposal into a $G \times G \times G$ regular sub-voxel grid. The center point of each sub-voxel is used as the grid point for the corresponding sub-voxel. However, since three-dimensional (3D) feature volumes are extremely sparse (less than 3% of voxels are not empty), max pooling over features of each sub-voxel cannot be directly utilized.
Instead, features are integrated from neighboring voxels into the grid points for feature extraction. To accomplish this, given a grid point $g_i$, voxel query is used to group a set of neighboring voxels $\Gamma_i=\left(\mathbf{v}_i^1, \mathbf{v}_i^2, \cdots, \mathbf{v}_i^K\right)$. Next, the neighboring voxel features are aggregated using a PointNet module $\mathrm{a}$, as shown in the following equation:
$$ \mathbf{\eta}_i=\max_{k=1,2,\cdots,K}\left(\Psi\left(\left[\mathbf{v}_i^k-\mathbf{g}_i ; \mathbf{\phi}_i^k\right]\right)\right) $$
Here, $\mathbf{v}_i-\mathbf{g}_i$ represents the relative coordinates of the voxel, $\mathbf{\phi}_i^k$ is the voxel feature of $\mathbf{v}_i^k$, and $\Psi(\cdot)$ indicates a multi-layer perceptron (MLP). The max pooling operation is performed along the channels to obtain the aggregated feature vector $\eta_i$. Voxel RoI Pooling is exploited to extract voxel features from the 3D feature volumes out of the last two stages in the $3 \mathrm{D}$ backbone network. For each stage, two Manhattan distance thresholds are set to group voxels with multiple scales. The aggregated features pooled from different stages and scales are then concatenated to obtain the RoI features.
Applications of Voxel RoI Pooling
Voxel RoI Pooling has become an essential component of object detection and classification in three-dimensional images or videos. It has been used in various industrial and medical applications, including manufacturing, robotics, and medical diagnosis. These applications require high accuracy and precise classification, and Voxel RoI Pooling provides the necessary features for these tasks. Additionally, due to its ability to handle sparsity in 3D feature volumes, Voxel RoI Pooling is useful in identifying objects in cluttered and complex environments, such as construction sites or crowded streets.
Voxel RoI Pooling is a powerful algorithm in computer vision that allows for precise identification and classification of objects in three-dimensional images or videos. Its ability to handle sparse 3D feature volumes and group neighboring voxels makes it ideal for industrial and medical applications that require high accuracy and precise classification. By utilizing Voxel RoI Pooling, companies and researchers can develop new technologies and applications that improve safety and increase efficiency.
