论文标题
使用多个阶段的方法进行单眼3D对象检测，并将注意力切成辅助的超级推理
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference
论文作者
论文摘要
3D对象检测至关重要，因为它可以使我们捕获对象的大小，定向和位置。结果，我们将能够在现实世界中使用此3D检测，例如增强现实（AR），自动驾驶汽车和机器人技术，这些机器人与人类相同的方式感知世界。单眼3D对象检测是在单个2D RGB图像中围绕对象绘制3D边界框的任务。它是本地化任务，但没有任何其他信息，例如深度或其他传感器或多个图像。单眼3D对象检测是一项重要但具有挑战性的任务。除了基于图像的2D对象检测的重大进展之外，对现实世界对象的3D理解是一个开放的挑战，到目前为止尚未广泛探索。除了最密切相关的研究。
3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies.
