Method

Next-generation Accurate and Efficient Monocular 3D Object Detection Method Accuracy [monodetrnext-a]
[Anonymous Submission]

Submitted on 13 May. 2024 13:37 by
[Anonymous Submission]

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We also observed that in MonoDETR, the acquisition
of depth information is overly simplistic, which
is crucial for monocular 3D detection models since
depth information is one of the few available 3D
data types. The accuracy of depth information is
closely linked to the precision of detection. To
improve depth information acquisition, we referred
to established 3D depth estimation networks and
designed a depth predictor that directly extracts
depth information from images. We integrated this
predictor with MonoDETRNext-F to develop
MonoDETRNext-A, which offers enhanced detection
capabilities.
Parameters:
~40M
Latex Bibtex:

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) 88.93 % 76.08 % 69.50 %
Car (Orientation) 88.49 % 75.03 % 68.38 %
Car (3D Detection) 29.94 % 24.14 % 23.79 %
Car (Bird's Eye View) 37.32 % 30.68 % 31.29 %
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