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.

本文提出了基于 Transformer 神经网络，通过直接估计的方式在 YCB-Video 数据集上进行 6D 多物体姿态估计，实现了最快的推理时间和与最先进方法相当的姿态估计精度。