Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
This addresses the problem of accurate 3D pose estimation for robotics and computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles 3D object pose estimation by proposing an end-to-end Siamese Regression Network that directly regresses poses and learns discriminative features, achieving state-of-the-art performance with high accuracy on a novel hand-object dataset under severe occlusions.
In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging problem of regression in angle space indirectly, focusing on learning discriminative features that are later fed into a separate architecture for 3D pose estimation. In contrast, we propose an end-to-end learning framework for directly regressing object poses by exploiting Siamese Networks. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset.