Method

MM-Omni3D: A Composite Benchmark for Universal Multi-Modal 3D Object Detection [MuTOD]
[Anonymous Submission]

Submitted on 30 Jan. 2024 08:50 by
[Anonymous Submission]

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We train a naive BEV multi-modal detector with our
developed MM-Omni3D dataset (a composite dataset
covering diverse scenes) and achieve promising
result.
Parameters:
100.8M
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) 98.78 % 97.69 % 94.62 %
Car (Orientation) 98.63 % 97.44 % 94.30 %
Car (3D Detection) 91.23 % 84.81 % 81.44 %
Car (Bird's Eye View) 95.69 % 91.51 % 88.71 %
This table as LaTeX

2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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