Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference
This addresses a challenging localization problem for robotics and autonomous systems, but appears incremental based on existing methods.
The paper tackles monocular 3D object detection from single RGB images, aiming to enable applications like AR and self-driving cars, but does not report specific results or numbers.
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.