Lightweight 3-D Convolutional Occupancy Networks for Virtual Object Reconstruction.

The increasing demand for edge devices causes the necessity for recent technologies to be adaptable to nonspecialized hardware. In particular, in the context of augmented, virtual reality, and computer graphics, the 3-D object reconstruction task from a sparse point cloud is highly computationally demanding and for this reason, it is difficult to accomplish on embedded devices. In addition, the majority of earlier works have focused on mesh quality at the expense of speeding up the creation process. In order to find the best balance between time for mesh generation and mesh quality, we aim to tackle the object reconstruction process by developing a lightweight implicit representation. To achieve this goal, we leverage the use of convolutional occupancy networks. We show the effectiveness of the proposed approach through extensive experiments on the ShapeNet dataset using systems with different resources such as GPU, CPU, and an embedded device.