SABER-6D: Shape Representation Based Implicit Object Pose Estimation
This work addresses pose estimation for various objects, including symmetric ones, without needing symmetry labels or additional labeled training data, though it appears incremental as it builds on existing shape-based methods.
The authors tackled the problem of 6D object pose estimation from RGB images by proposing SABER, an encoder-decoder architecture that learns shape representation at given poses, achieving close to benchmark results on Occlusion-LineMOD and T-LESS datasets.
In this paper, we propose a novel encoder-decoder architecture, named SABER, to learn the 6D pose of the object in the embedding space by learning shape representation at a given pose. This model enables us to learn pose by performing shape representation at a target pose from RGB image input. We perform shape representation as an auxiliary task which helps us in learning rotations space for an object based on 2D images. An image encoder predicts the rotation in the embedding space and the DeepSDF based decoder learns to represent the object's shape at the given pose. As our approach is shape based, the pipeline is suitable for any type of object irrespective of the symmetry. Moreover, we need only a CAD model of the objects to train SABER. Our pipeline is synthetic data based and can also handle symmetric objects without symmetry labels and, thus, no additional labeled training data is needed. The experimental evaluation shows that our method achieves close to benchmark results for both symmetric objects and asymmetric objects on Occlusion-LineMOD, and T-LESS datasets.