Method

4D-IteraStereo: Stereo Matching via Cost Aggregation and Iterative Optimization [4D-IteraStereo]
[Anonymous Submission]

Submitted on 4 Jan. 2025 03:32 by
[Anonymous Submission]

Running time:0.3 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
The 4D-IteraStereo method introduces a novel
approach to stereo matching by optimizing cost
aggregation and enhancing matching accuracy,
particularly in texture-less regions. Unlike
traditional methods that suffer from redundant
features and lack geometric information, 4D-
IteraStereo builds a lightweight 4D cost volume
and uses parallel asymmetric and dilated
convolutions for efficient cost aggregation. This
innovative design reduces computational load and
provides better regularization of the cost volume.
Furthermore, by integrating global and scene-prior
knowledge, the method effectively improves
accuracy in challenging texture-less areas and
fine structures. Experimental results confirm that
the proposed method significantly improves the
accuracy of stereo measurements in texture-less
scenes.
Parameters:
NAME: AdamW
LR: &lr 0.0002
WEIGHT_DECAY: 1.0e-05
EPS: 1.0e-08
Latex Bibtex:

Detailed Results

This page provides detailed results for the method(s) selected. For each of the first 20 test images, the number of erroneous pixels at all thresholds is depicted in the table. Underneath, the left input image, the disparity / end-point error map and the estimated (and interpolated) disparity / optical flow map are shown. The error map scales linearly between 0 (black) and >=5 (white) pixels error. Red denotes all occluded pixels, falling outside the image boundaries. The false color map is scaled to the largest ground truth disparity / flow value.

Test Set Average

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Reflective Regions

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Test Image 0

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.22 % 1.45 % 0.4 px 0.4 px
3 pixels 0.99 % 1.23 % 0.4 px 0.4 px
4 pixels 0.82 % 1.06 % 0.4 px 0.4 px
5 pixels 0.70 % 0.95 % 0.4 px 0.4 px
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Test Image 1

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 2.82 % 3.89 % 0.5 px 0.6 px
3 pixels 1.75 % 2.81 % 0.5 px 0.6 px
4 pixels 1.42 % 2.48 % 0.5 px 0.6 px
5 pixels 1.13 % 2.19 % 0.5 px 0.6 px
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Test Image 2

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.69 % 0.73 % 0.3 px 0.3 px
3 pixels 0.51 % 0.56 % 0.3 px 0.3 px
4 pixels 0.44 % 0.49 % 0.3 px 0.3 px
5 pixels 0.39 % 0.43 % 0.3 px 0.3 px
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Test Image 3

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.89 % 1.13 % 0.3 px 0.3 px
3 pixels 0.29 % 0.31 % 0.3 px 0.3 px
4 pixels 0.13 % 0.15 % 0.3 px 0.3 px
5 pixels 0.12 % 0.14 % 0.3 px 0.3 px
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Test Image 4

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.61 % 0.85 % 0.3 px 0.3 px
3 pixels 0.35 % 0.48 % 0.3 px 0.3 px
4 pixels 0.28 % 0.40 % 0.3 px 0.3 px
5 pixels 0.16 % 0.27 % 0.3 px 0.3 px
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Test Image 5

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.88 % 0.97 % 0.3 px 0.3 px
3 pixels 0.55 % 0.60 % 0.3 px 0.3 px
4 pixels 0.38 % 0.41 % 0.3 px 0.3 px
5 pixels 0.33 % 0.35 % 0.3 px 0.3 px
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Test Image 6

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 3.59 % 4.20 % 0.5 px 0.6 px
3 pixels 1.64 % 1.93 % 0.5 px 0.6 px
4 pixels 1.12 % 1.29 % 0.5 px 0.6 px
5 pixels 0.93 % 1.04 % 0.5 px 0.6 px
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Test Image 7

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.72 % 2.49 % 0.5 px 0.6 px
3 pixels 1.20 % 1.56 % 0.5 px 0.6 px
4 pixels 0.99 % 1.20 % 0.5 px 0.6 px
5 pixels 0.88 % 1.06 % 0.5 px 0.6 px
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Test Image 8

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.22 % 1.40 % 0.3 px 0.3 px
3 pixels 0.82 % 0.87 % 0.3 px 0.3 px
4 pixels 0.64 % 0.67 % 0.3 px 0.3 px
5 pixels 0.54 % 0.57 % 0.3 px 0.3 px
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Test Image 9

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.35 % 2.46 % 0.4 px 0.6 px
3 pixels 0.80 % 1.21 % 0.4 px 0.6 px
4 pixels 0.59 % 0.94 % 0.4 px 0.6 px
5 pixels 0.53 % 0.84 % 0.4 px 0.6 px
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Test Image 10

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.47 % 1.62 % 0.4 px 0.4 px
3 pixels 0.93 % 1.09 % 0.4 px 0.4 px
4 pixels 0.69 % 0.85 % 0.4 px 0.4 px
5 pixels 0.62 % 0.77 % 0.4 px 0.4 px
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Test Image 11

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.20 % 1.16 % 0.4 px 0.4 px
3 pixels 0.62 % 0.60 % 0.4 px 0.4 px
4 pixels 0.44 % 0.42 % 0.4 px 0.4 px
5 pixels 0.38 % 0.36 % 0.4 px 0.4 px
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Test Image 12

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 2.52 % 3.40 % 0.5 px 0.6 px
3 pixels 1.61 % 2.28 % 0.5 px 0.6 px
4 pixels 1.32 % 1.87 % 0.5 px 0.6 px
5 pixels 1.21 % 1.66 % 0.5 px 0.6 px
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Test Image 13

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 1.29 % 1.67 % 0.4 px 0.4 px
3 pixels 0.95 % 1.02 % 0.4 px 0.4 px
4 pixels 0.77 % 0.78 % 0.4 px 0.4 px
5 pixels 0.70 % 0.70 % 0.4 px 0.4 px
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Test Image 14

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.23 % 1.15 % 0.2 px 0.3 px
3 pixels 0.16 % 1.06 % 0.2 px 0.3 px
4 pixels 0.13 % 1.03 % 0.2 px 0.3 px
5 pixels 0.11 % 1.00 % 0.2 px 0.3 px
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Test Image 15

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 2.41 % 2.43 % 0.4 px 0.4 px
3 pixels 1.43 % 1.40 % 0.4 px 0.4 px
4 pixels 0.73 % 0.71 % 0.4 px 0.4 px
5 pixels 0.44 % 0.43 % 0.4 px 0.4 px
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Test Image 16

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 2.02 % 1.99 % 0.4 px 0.4 px
3 pixels 0.92 % 0.90 % 0.4 px 0.4 px
4 pixels 0.53 % 0.52 % 0.4 px 0.4 px
5 pixels 0.37 % 0.37 % 0.4 px 0.4 px
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Test Image 17

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 3.11 % 3.29 % 0.5 px 0.5 px
3 pixels 2.23 % 2.31 % 0.5 px 0.5 px
4 pixels 1.73 % 1.82 % 0.5 px 0.5 px
5 pixels 1.51 % 1.60 % 0.5 px 0.5 px
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Test Image 18

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.38 % 0.38 % 0.2 px 0.2 px
3 pixels 0.27 % 0.27 % 0.2 px 0.2 px
4 pixels 0.22 % 0.22 % 0.2 px 0.2 px
5 pixels 0.13 % 0.13 % 0.2 px 0.2 px
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Test Image 19

Error Out-Noc Out-All Avg-Noc Avg-All
2 pixels 0.81 % 0.79 % 0.3 px 0.3 px
3 pixels 0.54 % 0.52 % 0.3 px 0.3 px
4 pixels 0.42 % 0.40 % 0.3 px 0.3 px
5 pixels 0.35 % 0.34 % 0.3 px 0.3 px
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