  Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D
detection by enhancing the capability of perceiving depth with depth estimation
networks, and using LiDAR-based 3D detection architectures. The advanced stereo
3D detectors can also accurately localize 3D objects. The gap in image-to-image
generation for stereo views is much smaller than that in image-to-LiDAR
generation. Motivated by this, we propose a Pseudo-Stereo 3D detection
framework with three novel virtual view generation methods, including
image-level generation, feature-level generation, and feature-clone, for
detecting 3D objects from a single image. Our analysis of depth-aware learning
shows that the depth loss is effective in only feature-level virtual view
generation and the estimated depth map is effective in both image-level and
feature-level in our framework. We propose a disparity-wise dynamic convolution
with dynamic kernels sampled from the disparity feature map to filter the
features adaptively from a single image for generating virtual image features,
which eases the feature degradation caused by the depth estimation errors. Till
submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks
1st on car, pedestrian, and cyclist among the monocular 3D detectors with
publications on the KITTI-3D benchmark. The code is released at
https://github.com/revisitq/Pseudo-Stereo-3D.
