Monocular 3D Object Detection with Depth from Motion

Understand

Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings.

• It is notably difficult as a single image can not provide any clues for predicting absolute depth values.
• Motivated by binocular methods for 3D object detection, we take advantage of the strong geometry structure provided by camera ego-motion for accurate object depth estimation and detection.
• We first make a theoretical analysis on this general two-view case and notice two challenges: 1) Cumulative errors from multiple estimations that make the direct prediction intractable; 2) Inherent dilemmas caused by static cameras and matching ambiguity.