Monocular 3D Point Cloud Reconstruction

As with most AI methods, a 3D deep neural network needs to be trained to properly interpret its input data. More specifically, training a network for monocular 3D point cloud reconstruction requires a large set of recognized high-quality data which can be challenging to obtain. Hence, this dataset contains the image of a known object alongside its corresponding 3D point cloud representation. To collect a large number of categorized 3D objects, we use the ShapeNetCore (https://shapenet.org) dataset. It is a densely annotated subset of the ShapeNet dataset comprising 55 common object categories with 51,300 unique synthetic 3D models. These 3D models, however, were in mesh format and needed to be converted to the ground truth 3D point cloud. To convert these 3D mesh models to point clouds and to capture a single image from them, we used the Open3D library (https://www.open3d.org). The objects were positioned at the center of the scene and an image of each object was exported using a fixed-view camera for all objects. This way, we collectively prepared ~16000 data pairs in 8 groups of datasets in categories of the airplane, bottle, car, bench, sofa, cellphone, bike, and speaker. We dedicated a subdivision of the data in each category to the training (85%), validation (5%), and test (10%) phases.