从真实视图到渲染视图的深度样本2D-3D检测
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
摘要：
本文提出了一种用于2D-3D样本检测的端到端卷积神经网络（CNN）。我们证明，能够适应自然图像的功能，以更好地与CAD渲染的意见是至关重要的，我们的技术的成功。我们表明，适应可以通过合成自然图像上的纹理对象模型的渲染视图来学习。我们的方法可以自然地纳入CNN检测管道，并将深度学习的最新进展的准确性和速度优势扩展到2D-3D样本检测。我们将我们的方法应用于两个任务：实例检测，我们在IKEA数据集上进行评估，以及对象类别检测，我们在Pascal VOC数据集的子集上进行“椅子”检测时优于Aubry等人。
英文摘要：
This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset, and object category detection, where we out-perform Aubry et al. for "chair" detection on a subset of the Pascal VOC dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
