The KITTI Vision Benchmark Suite

We propose StereoDETR, an efficient stereo 3D
object detection framework based on DETR.
StereoDETR consists of two branches: a monocular
DETR branch and a stereo branch. The DETR branch
is built upon 2D DETR with additional channels for
predicting object scale, orientation, and sampling
points. The stereo branch leverages low-cost
multi-scale disparity features to predict object-
level depth maps. These two branches are coupled
solely through a differentiable depth sampling
strategy. It achieves binocular-level accuracy
while maintaining monocular-level inference speed.

\batch_size=16

@misc{mu2025stereodetrstereobasedtransformer3d,
title={StereoDETR: Stereo-based Transformer
for 3D Object Detection},
author={Shiyi Mu and Zichong Gu and Zhiqi Ai
and Anqi Liu and Yilin Gao and Shugong Xu},
year={2025},
eprint={2511.18788},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.18788},
}