ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation (2203.09418v2)

Abstract: Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.

• The paper introduces a hierarchical binary encoding approach for robust 6DoF pose estimation from RGB images.
• It employs a coarse-to-fine learning strategy that progressively refines 2D-3D correspondences to minimize errors even under occlusion.
• Validated on LM-O and YCB-V datasets, ZebraPose achieves state-of-the-art accuracy, rivaling traditional RGB-D methods.

Overview of ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation

The paper "ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation" presents a novel dense correspondence framework for estimating the 6 Degree-of-Freedom (6DoF) pose of objects from RGB images. The authors introduce a robust method that efficiently encodes an object's surface using a hierarchical binary descriptor. This approach is designed to address challenges in object pose estimation, such as occlusion and the lack of texture, which have historically led to reduced accuracy in RGB-based methods compared to depth-based approaches.

Methodological Contributions

ZebraPose advances the 6DoF object pose estimation domain by focusing on dense 2D-3D correspondences and tackling it as a hierarchical classification task. The method comprises three main stages:

1. Surface Encoding: The paper proposes a binary numeral system for encoding the object surface, dividing the surface into groups iteratively in multiple hierarchical levels. This approach ensures a compact representation and a bijective mapping from 2D pixel information to 3D surface vertices, facilitating one-to-one correspondences.
2. Coarse to Fine Learning Strategy: Leveraging the hierarchical nature of the binary encoding, the authors introduce a tailored loss function and training strategy that prioritize learning coarse correspondences at initial stages and gradually refines the predictions to finer levels. This hierarchical learning minimizes prediction errors due to the uniform distribution of focus across hierarchical levels.
3. Pose Estimation: By matching the predicted descriptors with a lookup table of the 3D model, ZebraPose efficiently identifies 2D-3D matches, which feed into a Perspective-n-Points (PnP) algorithm enhanced with the Progressive-X solver. This framework's unique compact descriptor design ensures the predicted correspondences align with actual object surface points, thus enhancing pose estimation accuracy.

Results and Implications

The paper reports significant improvements in pose estimation accuracy on the widely-used LM-O and YCB-V datasets, surpassing previous state-of-the-art methods under the ADD(-S) metric. Notably, ZebraPose demonstrates its efficacy even when compared to RGB-D methods, which traditionally perform better due to the additional depth data.

The implications of this work are multifaceted. Practically, the ability of ZebraPose to offer high-precision 6DoF pose estimation using only RGB input expands its utility in scenarios where depth sensing is impractical, such as in augmented reality and unconstrained robotic environments. Theoretically, the hierarchical encoding and coarse-to-fine training paradigm may inspire further research into efficient and effective pose estimation techniques, especially those that leverage binary configurations for mapping complex 3D geometries.

Future Directions

While ZebraPose achieves promising results, future research could explore its integration into broader AI systems, including its application in dynamic environments and its adaptation for category-level object pose estimation. Additionally, investigating alternative encoding schemes or learning architectures may further enhance the system's robustness and generalization capabilities.

By advancing the state of the art in RGB-based pose estimation, ZebraPose represents a significant step forward in this fundamental area of computer vision, opening new avenues for applications and research alike.