Discriminative 3D Shape Modeling for Few-Shot Instance Segmentation

In this paper, we present a simple and efficient scheme for segmenting approximately convex 3D object instances in depth images in a few-shot setting via discriminatively modeling the 3D shape of the object using a neural network. Our key idea is to select pairs of 3D points on the depth image between which we compute surface geodesics. As the number of such geodesics is quadratic in the number of image pixels, we can create a large training set of geodesics using only very limited ground truth instance annotations. These annotations are used to create a binary label for each geodesic, which indicates whether or not that geodesic belongs entirely to one instance segment. A neural network is then trained to classify the geodesics using these labels. During inference, we create geodesics from selected seed points in the test depth image, then produce a convex hull of the points that are classified by the neural network as belonging to the same instance, thereby achieving instance segmentation. We present experiments applying our method to segmenting instances of food items in real-world depth images. Our results demonstrate promising performances compared to prior methods in accuracy and computational efficiency.