Method

MonoCD [MonoCD]
https://github.com/elvintanhust/MonoCD

Submitted on 11 Mar. 2026 08:12 by
Taewook Eum (SKKU)

Running time:0.04 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Monocular 3D object detection using
complementary depths.
The model predicts multiple depth estimates from
different
geometric representations and combines them
through a
learned depth ensemble. Built on CenterNet-
based architecture
with DLA-34 backbone. Trained on KITTI training
split.
Parameters:
None
Latex Bibtex:
@inproceedings{yan2024monocd,
title={MonoCD: Monocular 3D Object Detection
with Complementary Depths},
author={Yan, Longfei and Yan, Pei and Xiong,
Shenghua and Xiang, Xuanyu and Tan, Yihua},
booktitle={CVPR},
year={2024}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 84.18 % 61.65 % 49.38 %
Car (Orientation) 84.07 % 61.51 % 49.25 %
Car (3D Detection) 13.10 % 9.03 % 6.92 %
Car (Bird's Eye View) 22.65 % 15.60 % 12.76 %
This table as LaTeX


2D object detection results.
This figure as: png eps txt gnuplot



Orientation estimation results.
This figure as: png eps txt gnuplot



3D object detection results.
This figure as: png eps txt gnuplot



Bird's eye view results.
This figure as: png eps txt gnuplot




eXTReMe Tracker