3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs

3D scene graph prediction is a task that aims to concurrently predict object classes and their relationships within a 3D environment. As these environments are primarily designed by and for humans, incorporating commonsense knowledge regarding objects and their relationships can significantly constrain and enhance the prediction of the scene graph. In this paper, we investigate the application of commonsense knowledge graphs for 3D scene graph prediction on point clouds of indoor scenes. Through experiments conducted on a real-world indoor dataset, we demonstrate that integrating external commonsense knowledge via the message-passing method leads to a 15.0 % improvement in scene graph prediction accuracy with external knowledge and $7.96\%$ with internal knowledge when compared to state-of-the-art algorithms. We also tested in the real world with 10 frames per second for scene graph generation to show the usage of the model in a more realistic robotics setting.

Target driven visual navigation exploiting object relationships

Recently target driven visual navigation strategies have gained a lot of popularity in the computer vision and reinforcement learning community. Unfortunately, most of the current research tends to incorporate sensory input into a reward-based learning approach, with the hope that a robot can implicitly learn its optimal actions through recursive trials. These methods seldom generalize across domains as they fail to exploit natural environment object relationships. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven visual navigation algorithm, which considers the inherent relationship between "target" objects, along with the more salient "parent" objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show $\approx \textbf{30 %}$ improvement over the existing state-of-the-art navigation methods in terms of the success rate. We also show that our model learns to converge much faster than other algorithms. We will make our code publicly available for use in the scientific community.