EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation (2303.12787v3)

Abstract: Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. Yet, learning the entire correspondences from scratch is highly challenging, particularly for ambiguous pose solutions, where the globally optimal pose is theoretically non-differentiable w.r.t. the points. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle generalizes previous approaches, and resembles the attention mechanism. EPro-PnP can enhance existing correspondence networks, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation benchmark. Furthermore, EPro-PnP helps to explore new possibilities of network design, as we demonstrate a novel deformable correspondence network with the state-of-the-art pose accuracy on the nuScenes 3D object detection benchmark. Our code is available at https://github.com/tjiiv-cprg/EPro-PnP-v2.

• The paper introduces a probabilistic PnP layer that models pose uncertainty over the SE(3) manifold to resolve ambiguity in object pose estimation.
• It employs a Monte Carlo KL loss with derivative regularization for stable, end-to-end learning without relying on explicit geometric supervision.
• Experimental results on benchmarks like LineMOD and nuScenes demonstrate that EPro-PnP significantly outperforms traditional methods with improved accuracy and network flexibility.

Overview of EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

In this paper, the authors propose EPro-PnP, an innovative probabilistic framework for solving the long-standing computer vision problem of monocular object pose estimation. The work is rooted in the Perspective-n-Point (PnP) problem, serving as a bridge between deep learning methodologies and classical geometric approaches. The authors point out that previous methods have struggled to effectively learn full 2D-3D correspondences due to the non-differentiable nature of the globally optimal poses in ambiguous circumstances.

Technical Contributions

The paper introduces a probabilistic PnP layer capable of generating a distribution over potential object poses. This distribution is expressed over the SE(3) manifold, enabling a differentiable probability density, which is crucial for learning entire sets of 2D-3D correspondences as intermediate variables by minimizing the Kullback-Leibler (KL) divergence between the predicted and the true pose distributions.

A crucial aspect of this research is how it discusses the treatment of the 2D-3D point correspondences and their associated weights as intermediate variables to be learned through backpropagation. The probabilistic interpretation resonates with the attention mechanism, offering a generalized approach that encompasses prior methodologies while innovating on effective learning.

The EPro-PnP surpasses traditional methods in pose ambiguity by introducing a Monte Carlo KL loss for end-to-end learning and a derivative regularization loss for training stability. These innovations enable EPro-PnP to work with ambiguous pose solutions, which are prevalent in situations involving symmetric objects or uncertain observations.

Results and Performance

The proposed method is tested extensively on standard benchmarks such as the LineMOD 6DoF pose estimation and the nuScenes 3D object detection dataset. In the experiments, EPro-PnP showcases its superiority by achieving state-of-the-art pose accuracy without requiring explicit geometric supervision or additional network overhead typically seen in past approaches.

Furthermore, the paper demonstrates that EPro-PnP enhances existing networks, permitting flexibility for innovative network designs. For instance, the integration of EPro-PnP with a novel deformable correspondence network establishes a new benchmark for pose accuracy, underlined by robust experimental results.

Implications and Future Directions

This work has significant implications for the fields of autonomous vehicles and robotic systems, where accurate 3D vision is pivotal. Practically, it reduces the reliance on complex manual annotations and geometric priors, offering a more scalable and flexible solution. The probabilistic aspect of EPro-PnP not only enhances robustness to dataset variances and noise but also allows for capturing uncertainty in pose estimation more effectively.

Given the versatility of the EPro-PnP framework, future research could explore extending this methodology to other vision tasks requiring pose estimation under uncertainty. Furthermore, integrating this probabilistic framework into other types of correspondences, such as feature-metric correlations, might yield even richer representations for 3D object pose estimation tasks.

In conclusion, the approach outlined in EPro-PnP marks a significant advancement in the development of end-to-end differentiable solutions for complex geometric problems in computer vision, setting the stage for further innovations in pose estimation and beyond.