Density-Based Clustering for 3D Object Detection in Point Clouds

Current 3D detection networks either rely on 2D object proposals or try to directly predict bounding box parameters from each point in a scene. While former methods are dependent on performance of 2D detectors, latter approaches are challenging due to the sparsity and occlusion in point clouds, making it difficult to regress accurate parameters. In this work, we introduce a novel approach for 3D object detection that is significant in two main aspects: a) cascaded modular approach that focuses the receptive field of each module on specific points in the point cloud, for improved feature learning and b) a class agnostic instance segmentation module that is initiated using unsupervised clustering. The objective of a cascaded approach is to sequentially minimize the number of points running through the network. While three different modules perform the tasks of background-foreground segmentation, class agnostic instance segmentation and object detection, through individually trained point based networks. We also evaluate bayesian uncertainty in modules, demonstrating the over all level of confidence in our prediction results. Performance of the network is evaluated on the SUN RGB-D benchmark dataset, that demonstrates an improvement as compared to state-of-the-art methods.