Learning Descriptors for Object Recognition and 3D Pose Estimation (1502.05908v2)

Abstract: Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.

• The paper introduces a CNN-based descriptor method that encapsulates both object identity and 3D pose in a compact 16-dimensional space.
• It employs innovative triplet and pair-wise constraints to train descriptors that accurately reflect pose similarities using Euclidean distance.
• Empirical results show significant improvements over traditional methods, enhancing scalability for real-world applications like robotics and augmented reality.

Learning Descriptors for Object Recognition and 3D Pose Estimation

The paper authored by Wohlhart and Lepetit presents a novel approach to the challenging task of object recognition and 3D pose estimation, particularly for poorly textured objects. Traditional methods often struggle with scalability due to their reliance on one classifier per object or multi-class classifiers whose complexity increases with the number of objects. This research circumvents these limitations by leveraging Nearest Neighbor (NN) classification, facilitating scalability to large datasets while allowing easy addition or removal of objects.

Key Contributions

The core contribution of the paper lies in a new method for computing descriptors using a Convolutional Neural Network (CNN). These descriptors efficiently encapsulate both the object identity and 3D pose, allowing the use of Euclidean distance for similarity evaluations. This is particularly advantageous as it permits the application of scalable NN search mechanisms, previously unachievable with methods reliant on geodesic distances.

The authors introduce an innovative training protocol for the CNN, employing triplet and pair-wise constraints. This training setup encourages the network to minimize the distance between descriptors of the same object with similar poses while maximizing the distance between descriptors of different objects. By doing so, the method creates a descriptor space where the distance between descriptors significantly correlates with the pose similarity.

Numerical Results

Empirically, the proposed descriptors show substantial improvements in object recognition and pose estimation accuracy compared to state-of-the-art methods. The descriptors achieved near-perfect accuracy rates with minimal pose error across RGB, depth, and RGB-D data, even when using a compact 16-dimensional descriptor. These results outperform traditional descriptors like LineMOD and HOG, using far fewer descriptor dimensions—highlighting the efficiency of the approach.

Implications and Future Work

Practically, this research provides an efficient and scalable solution for real-world applications where numerous objects must be recognized and their poses estimated, such as in industrial robotics or augmented reality systems. The descriptors' compactness not only enhances computational efficiency but also facilitates deployment on resource-constrained platforms, such as mobile robots.

Theoretically, this work advances the understanding of how machine learning models can fulminate high-dimensional search problems by transforming them into well-structured low-dimensional spaces.

Future directions could explore improvements in generalization to completely unseen objects, with preliminary results indicating promising capabilities. Additionally, the integration of this approach with other sensory modalities and its application to dynamic environments could extend its utility and robustness in more complex and variable settings.

In conclusion, the paper proposes a robust framework for addressing large-scale object recognition and 3D pose estimation problems through innovative descriptor learning. The methodology stands out for its scalability, efficiency, and high performance, paving the way for further advancements in this domain of computer vision.