DeepIM: Deep Iterative Matching for 6D Pose Estimation (1804.00175v4)

Abstract: Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the observed image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.

DeepIM: Deep Iterative Matching for 6D Pose Estimation

The paper introduces DeepIM, a novel methodology addressing the critical challenge of accurate 6D pose estimation from RGB images. This task is pivotal for applications in robotic manipulation and virtual reality where precise localization and orientation of objects are essential.

Methodological Advancements

DeepIM offers a significant improvement over conventional pose estimation techniques by leveraging a deep neural network to refine initial pose estimates iteratively. Unlike prior models that rely heavily on direct regression or handcrafted features, DeepIM employs a process of pose refinement through iterative matching of rendered synthetic images to observed images. This refinement is achieved by predicting a relative SE(3) transformation that adjusts the initial pose.

Core Contributions

The primary contributions of the paper are as follows:

1. Iterative Refinement via Deep Learning: The proposed network iteratively enhances pose estimation, with large improvements over existing state-of-the-art methods on benchmarks such as LINEMOD and Occlusion LINEMOD.
2. Disentangled Pose Representation: The paper introduces a disentangled representation of the SE(3) transformation, effectively separating 3D location and orientation prediction. This representation facilitates refining poses of previously unseen objects.
3. Robustness to Varied Conditions: DeepIM shows robustness in handling objects with diverse appearances due to lighting changes and occlusions, which are common challenges in RGB-based pose estimation.

Numerical Results

The experiments conducted demonstrate substantial improvements in accuracy metrics. For instance, DeepIM achieves an accuracy of 85.2% on the rigorous 5°/5cm metric when tested on the LINEMOD dataset, significantly outperforming previous methods. Similar trends are observed across different metrics like 6D Pose and 2D Projection, showcasing the efficacy of the iterative approach.

Implications and Future Directions

From a practical standpoint, DeepIM's ability to operate effectively with only RGB input reduces reliance on depth sensors, which can be limited by resolution and range constraints. This introduces new possibilities for deploying high fidelity cameras in dynamic environments.

Theoretically, the separation of pose transformations into disentangled components marks a paradigm shift in pose estimation. This could inform future research into more generalized object detection and tracking systems. An exploration into extending DeepIM to stereo or multi-view setups might further enhance its accuracy and applicability.

DeepIM's approach lays a strong foundation for future developments in AI-powered applications requiring real-time, accurate object pose estimation. Continued exploration into the scalability of this system, particularly its adaptation for highly complex and cluttered scenes, remains an exciting avenue for research.

Overall, the introduction of DeepIM represents a substantial technical advancement in the field of 6D pose estimation, reflecting adept integration of deep learning innovations to address longstanding challenges in the field.