We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a "holistic" approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the corners of their 3D bounding boxes for the pose of objects' parts. This, however, is not sufficient for handling objects from the recent T-LESS dataset: These objects exhibit an axis of rotational symmetry, and the similarity of two images of such an object under two different poses makes training the CNN challenging. We solve this problem by restricting the range of poses used for training, and by introducing a classifier to identify the range of a pose at run-time before estimating it. We also use an optional additional step that refines the predicted poses for hand pose estimation. We improve the state-of-the-art on the LINEMOD dataset from 73.7% to 89.3% of correctly registered RGB frames. We are also the first to report results on the Occlusion dataset using color images only. We obtain 54% of frames passing the Pose 6D criterion on average on several sequences of the T-LESS dataset, compared to the 67% of the state-of-the-art on the same sequences which uses both color and depth. The full approach is also scalable, as a single network can be trained for multiple objects simultaneously.

本研究针对T-LESS数据集中具有旋转对称轴的物体进行了3D目标检测和姿态估计，通过引入CNN等技术，实现了色彩图像的2D投影识别和姿态预测方案，并使用限制范围的训练和分类器优化，最终在LINEMOD数据集上将正确注册的RGB帧从73.7％提高至89.3％，并对仅使用彩色图像的Occlusion数据集进行了测试。

BB8: 一种可扩展、准确、部分遮挡稳健的方法，用于预测具有挑战性的物体的3D姿态而无需使用深度