T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
This work addresses 6D pose estimation for robotics and AR applications, but it is incremental as it adapts an existing transformer architecture (DETR) to this specific task.
The authors tackled 6D pose estimation for robotics and augmented reality by proposing T6D-Direct, a transformer-based method that achieves the fastest inference time and comparable accuracy to state-of-the-art methods on the YCB-Video dataset.
6D pose estimation is the task of predicting the translation and orientation of objects in a given input image, which is a crucial prerequisite for many robotics and augmented reality applications. Lately, the Transformer Network architecture, equipped with a multi-head self-attention mechanism, is emerging to achieve state-of-the-art results in many computer vision tasks. DETR, a Transformer-based model, formulated object detection as a set prediction problem and achieved impressive results without standard components like region of interest pooling, non-maximal suppression, and bounding box proposals. In this work, we propose T6D-Direct, a real-time single-stage direct method with a transformer-based architecture built on DETR to perform 6D multi-object pose direct estimation. We evaluate the performance of our method on the YCB-Video dataset. Our method achieves the fastest inference time, and the pose estimation accuracy is comparable to state-of-the-art methods.