Deep Learning-Based Object Pose Estimation: A Comprehensive Survey (2405.07801v3)

Abstract: Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, \emph{i.e.}, instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at https://github.com/CNJianLiu/Awesome-Object-Pose-Estimation.

• The paper presents an in-depth survey dissecting deep learning methods across instance, category, and unseen object pose estimation to identify strengths and limitations.
• It compares correspondence-, template-, voting-, and regression-based methodologies, emphasizing their accuracy and computational trade-offs.
• The survey outlines future directions, highlighting the need for label-efficient and robust scalable models for diverse real-world conditions.

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

Object pose estimation is crucial for advancements in computer vision applications such as augmented reality, robotics, and automation. Over recent years, deep learning models have significantly overtaken traditional methods in this domain due to their superior performance metrics on aspects such as accuracy and robustness. This survey dissects the intricate landscape of deep learning-based object pose estimation techniques, offering valuable insights into the strengths, weaknesses, and trends shaping this research domain.

Instance-Level Object Pose Estimation

Instance-level object pose estimation methods historically dominated early deep learning research in this field. These methods particularly shine when applied to specific object instances present in the training data. The taxonomy of instance-level methods includes correspondence-based, template-based, voting-based, and regression-based approaches.

1. Correspondence-Based Methods: These methods focus on establishing precise correspondences across input image data and pre-existing CAD models, divided into sparse and dense correspondences. Sparse correspondence methods, while efficient, often suffer accuracy limitations due to their reliance on minimal control points. Dense correspondence-based techniques address this by establishing more comprehensive correspondences between object models and input data, yielding greater robustness to occlusion challenges.
2. Template-Based Methods: Template-based approaches index an array of rendered viewpoint templates, each tagged with a ground-truth pose, making these methods adept at managing texture-less objects. However, they grapple with memory and computational intensities as they increase in scale and complexity, highlighting a need for more efficient indexing and retrieval strategies.
3. Voting-Based Methods: These methods either employ indirect approaches, predicting key points and calculating poses via correspondences afterward, or direct strategies that predict object pose directly from learned image features. While improving accuracy and practical performance, voting-based approaches can scale computationally with increased environmental complexity.
4. Regression-Based Methods: These aim to directly regress the object pose utilizing deep representative features. They are categorized into geometry-guided and direct regression methods. Geometry-guided regression entails the integration of explicit geometric constraints aiding the optimization process, whereas direct regression approaches simplify the learning process by outputting pose estimates directly from deep network layers.

While effective, instance-level methods demand comprehensive training data for each object instance, reducing their scalability to novel object appearance without retraining.

Category-Level Object Pose Estimation

Category-level methods expand on instance-level capabilities by accommodating intra-class variability, allowing them to generalize over unseen instances within pre-defined categories. These are bifurcated into shape prior-based and shape prior-free methods.

1. Shape Prior-Based Methods: These techniques establish shape priors using CAD models to guide pose estimation. Methods such as the Normalized Object Coordinate Space (NOCS) approach fundamentally enhance pose prediction by facilitating spatial reconstructions that augment pose estimations. Direct regression models extend this by integrating shape prior knowledge into the learning processes, offering end-to-end optimization capabilities.
2. Shape Prior-Free Methods: Alternatively, shape prior-free methods emphasize semantic and geometric feature fusions, enabling them to process poses without the NOCS or shape prior constructs. They demonstrate strength in environments lacking CAD models or when geometrical perturbations present significant challenges.

Despite achievements within category-level research, retraining across new object categories remains pivotal, constraining their universality.

Unseen Object Pose Estimation

These methods pivot from the requirement of intensive retraining, focusing instead on model designs that generalize across entirely new objects without per-instance pose models. They can involve CAD models or manual reference views for unseen instances.

1. CAD Model-Based Methods: Utilizing object CAD models, these involve feature matching techniques for establishing pose predicates by aligning features between observed data and model datasets. Alternatively, template matching methods embed multi-view object templates to determine poses through render-and-compare strategies.
2. Manual Reference View-Based Methods: These approaches obviate the need for CAD models, leveraging sparse annotated views of target objects to build semantic correspondences or employ reconstructive matching for pose estimations.

Unseen object pose methodologies, while novel, still necessitate pre-obtained CAD models or reference annotations, limiting scalability and real-time adaptability.

Future Directions and Challenges

Despite significant advances, deep learning-based pose estimation faces challenges in computational scalability, universality across object types, and adaptation within diverse lighting and occlusion conditions. Future research might benefit from exploring:

• Enhancements in label-efficient methods, reducing dependency on extensive ground-truth datasets.
• Techniques increasing environmental adaptivity, emphasizing domains lacking controlled lighting or textual contrasts.
• Expanding the generalization of category-level methods to cover more diverse class-categories without performance reductions.

This survey aims to galvanize a deeper understanding among researchers, encouraging further investigations within these exciting dimensions of computer vision.