The KITTI Vision Benchmark Suite

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, three problems have been discovered in
existing practices: (1) relying on a high-
performance monocular depth estimator, (2) the
generated image suffering from visual holes,
deformations, and artifacts, and (3) being
difficult to be compatible with geometry-based
stereo detectors. In this work, we propose a novel
pseudo-stereo 3D detection framework without depth
estimation, called PS-SVDM. This framework
utilizes a diffusion model to generate a high-
quality virtual right view from a left image to
mimic the stereo camera signal. With this
representation, we can apply various existing
stereo image-based detection algorithms.

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@article{shi2023svdm,
title={SVDM: Single-View Diffusion Model for
Pseudo-Stereo 3D Object Detection},
author={Shi, Yuguang},
journal={arXiv preprint arXiv:2307.02270},
year={2023}
}