Method

SQD++ [SQD++]


Submitted on 22 May. 2025 09:50 by
mo yujian (tongji university)

Running time:0.08 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
Fully exploiting the potential of pseudo points requires accurate modeling of their local neighborhood information. However, this integration introduces computational overhead and structural ambiguity: the large number of pseudo points greatly increases processing costs, and those that appear adjacent in the 2D image space may be spatially distant in 3D, resulting in misleading neighborhood relationships and degraded feature representations.
Parameters:
\alpha=0.2
Latex Bibtex:
None

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) 98.47 % 95.84 % 93.03 %
Car (Orientation) 98.46 % 95.72 % 92.84 %
Car (3D Detection) 92.12 % 85.14 % 80.14 %
Car (Bird's Eye View) 95.72 % 92.03 % 88.92 %
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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