Abstract: A method for performing a 3D point cloud object detection is provided. The method includes: receiving an object data comprising a point cloud of a plurality of sensed points; performing a grid-based key-points extraction on raw data points of the point cloud, so as to obtain one or more key-points; performing a hierarchical point-wise convolution on the raw data points of the point cloud according to the key-points, so as to obtain a plurality of target point-wise features; performing a location-aware 3D RoI pooling operation according to the target point-wise features, so as to obtain pooled point-wise features and pooled 3D voxels corresponding to the pooled point-wise features; performing 3D convolution operations on the pooled 3D voxels, so as to obtain represent data points, wherein each of the represent data points includes a represent feature; and inputting the represent data points into multi-layer perceptrons to obtain final predictions.