  One of the key problems in 3D object detection is to reduce the accuracy gap
between methods based on LiDAR sensors and those based on monocular cameras. A
recently proposed framework for monocular 3D detection based on Pseudo-Stereo
has received considerable attention in the community. However, so far these two
problems are discovered in existing practices, including (1) monocular depth
estimation and Pseudo-Stereo detector must be trained separately, (2) Difficult
to be compatible with different stereo detectors and (3) the overall
calculation is large, which affects the reasoning speed. In this work, we
propose an end-to-end, efficient pseudo-stereo 3D detection framework by
introducing a Single-View Diffusion Model (SVDM) that uses a few iterations to
gradually deliver right informative pixels to the left image. SVDM allows the
entire pseudo-stereo 3D detection pipeline to be trained end-to-end and can
benefit from the training of stereo detectors. Afterwards, we further explore
the application of SVDM in depth-free stereo 3D detection, and the final
framework is compatible with most stereo detectors. Among multiple benchmarks
on the KITTI dataset, we achieve new state-of-the-art performance.
