Abstract
Volumetric grid is widely used for 3D deep learning due
to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it
needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally
inefficient. In this work, we propose the PointGrid, a 3D
convolutional network that incorporates a constant number
of points within each grid cell thus allowing the network to
learn higher order local approximation functions that could
better represent the local geometry shape details. With experiments on popular shape recognition benchmarks, PointGrid demonstrates state-of-the-art performance over existing deep learning methods on both classification and segmentation