Segmentation-driven 6D Object Pose Estimation (1812.02541v3)

Abstract: The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.

Overview of "Segmentation-driven 6D Object Pose Estimation"

The task of estimating the 6D pose of rigid objects, which involves determining three rotational and three translational parameters, is central to a variety of applications in robotics and augmented reality. Traditional methods typically rely on establishing correspondences between known 3D model points and their 2D projections, followed by pose computation via the Perspective-n-Point (PnP) algorithm. However, these approaches are often challenged by scenes with occlusions or poorly-textured objects.

In this paper, the authors introduce a novel framework that leverages segmentation to improve 6D pose estimation accuracy, particularly in cluttered environments. The method diverges from conventional strategies that predict a single global pose for an object. Instead, it employs a segmentation-driven mechanism where each visible part of an object independently predicts 2D keypoint locations. A confidence measure for each prediction is also estimated, allowing the system to robustly combine multiple estimates into accurate 3D-to-2D correspondences, leading to a reliable pose computation using a RANSAC-based PnP strategy.

The proposed method is evaluated on challenging datasets, including Occluded-LINEMOD and YCB-Video. It demonstrates superior performance, handling occlusions more effectively than state-of-the-art techniques. The architecture's simplicity allows for real-time operation, making it attractive for applications requiring immediate feedback.

Key Contributions

1. Segmentation-driven Pose Estimation: The introduction of local predictions based on segmented parts of an object to improve robustness against occlusions marks a significant departure from those that consider objects as single entities. This approach not only enhances robustness but also aligns segmentation with pose estimation, ensuring consistent object detection and pose computation.
2. Architecture and Efficiency: By optimizing for real-time performance, the method balances computational efficiency with accuracy. The architecture consists of a two-stream network sharing a common encoder with separate segmentation and keypoint regression paths, making it both flexible and efficient.
3. Robustness in Complex Scenes: The technique is particularly effective in occluded settings or in environments with multiple overlapping objects. It operates by integrating confidence measures for each projected keypoint, ensuring that only reliable estimates inform the final pose computation.

Experimental Results

The experimental section highlights strong numerical results, showcasing the method's superiority over other state-of-the-art approaches in challenging scenarios. On the Occluded-LINEMOD dataset, the method achieves higher accuracy in key pose estimation metrics such as ADD and REP when compared to adversaries like PoseCNN and BB8. The performance on YCB-Video reinforces these findings, demonstrating robustness in diverse and noisy environments.

Implications and Future Directions

Practically, the proposed framework could significantly enhance robotic manipulation tasks in cluttered scenarios by offering more reliable pose estimates without additional refinement steps. The theoretical implications suggest a promising direction in combining segmentation with pose estimation, potentially influencing future designs of neural networks for computer vision tasks.

Looking forward, the paper suggests that further exploration in adaptive grid utilization or integrating the PnP step within the network architecture could lead to even greater performance improvements. Additionally, enhancing the confidence estimation mechanism might bridge the gap towards optimal predictions, as indicated by the oracle performance benchmark in fusion strategies.

In conclusion, this paper presents a substantial advancement in handling occluded and cluttered environments for 6D object pose estimation, balancing high accuracy with computational efficiency, bearing significant implications for real-world deployment in robotics and augmented reality systems.