3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
This addresses the need for uncertainty quantification and robustness in 3D scene understanding for applications in computer vision and robotics, representing a novel method for a known bottleneck.
The paper tackles the problem of robust 6D pose estimation from RGB-D images by introducing probabilistic modeling into inverse graphics, resulting in performance on par with state-of-the-art on the YCB-Video dataset with improved robustness in challenging conditions.
The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understanding that aims to infer the 3D scene structure from 2D images. In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks. Specifically, we propose 3D Neural Embedding Likelihood (3DNEL) as a unified probabilistic model over RGB-D images, and develop efficient inference procedures on 3D scene descriptions. 3DNEL effectively combines learned neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D object pose estimation from RGB-D images. Performance on the YCB-Video dataset is on par with state-of-the-art yet is much more robust in challenging regimes. In contrast to discriminative approaches, 3DNEL's probabilistic generative formulation jointly models multiple objects in a scene, quantifies uncertainty in a principled way, and handles object pose tracking under heavy occlusion. Finally, 3DNEL provides a principled framework for incorporating prior knowledge about the scene and objects, which allows natural extension to additional tasks like camera pose tracking from video.