Method

Robust Stereo Vision-Based 3D Object Detection [SG3D]
[Anonymous Submission]

Submitted on 25 Feb. 2026 09:51 by
[Anonymous Submission]

Running time:0.15 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This method proposes a robust 3D object detection
framework based on binocular stereo vision. To
enhance robustness in complex scenarios, high-
fidelity disparity maps are first estimated using
a dense stereo matching network and converted into
pseudo-LiDAR point clouds. A systematic
sparsification strategy is then applied to reduce
noise and computational overhead. Finally, a
robust voxel-based 3D detection network is
employed to extract precise bounding boxes from
the sparsified stereo point clouds.
Parameters:
stereo
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) 95.97 % 83.98 % 74.84 %
Car (Orientation) 95.74 % 83.25 % 74.07 %
Car (3D Detection) 74.23 % 47.91 % 42.93 %
Car (Bird's Eye View) 81.73 % 57.65 % 51.21 %
Pedestrian (Detection) 46.07 % 32.59 % 29.87 %
Pedestrian (Orientation) 37.07 % 25.87 % 23.48 %
Pedestrian (3D Detection) 28.40 % 19.20 % 16.98 %
Pedestrian (Bird's Eye View) 30.80 % 21.31 % 18.89 %
Cyclist (Detection) 49.28 % 29.69 % 26.14 %
Cyclist (Orientation) 37.46 % 21.97 % 19.37 %
Cyclist (3D Detection) 34.82 % 20.16 % 17.63 %
Cyclist (Bird's Eye View) 36.57 % 21.25 % 18.61 %
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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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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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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