  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. Accordingly, we
establish the stereo correspondence with a geometry-aware cost volume as the
alternative for depth estimation and further compensate it with monocular
understanding to address the second problem. Our framework, named Depth from
Motion (DfM), then uses the established geometry to lift 2D image features to
the 3D space and detects 3D objects thereon. We also present a pose-free DfM to
make it usable when the camera pose is unavailable. Our framework outperforms
state-of-the-art methods by a large margin on the KITTI benchmark. Detailed
quantitative and qualitative analyses also validate our theoretical
conclusions. The code will be released at
https://github.com/Tai-Wang/Depth-from-Motion.
