Method

nlspn_bs8_retrain [nlspn_bs8_retrain]
[Anonymous Submission]

Submitted on 21 Jan. 2023 12:28 by
[Anonymous Submission]

Running time:0.27 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
The proposed network takes RGB and sparse depth
images as inputs and estimates non-local neighbors
and their affinities of each pixel, as well as an
initial depth map with pixel-wise confidences. The
initial depth prediction is then iteratively
refined by its confidence and non-local spatial
propagation procedure based on the predicted non-
local neighbors and corresponding affinities.
Unlike previous algorithms that utilize fixed-
local neighbors, the proposed algorithm
effectively avoids irrelevant local neighbors and
concentrates on relevant non-local neighbors
during propagation.
Parameters:
batch size=8, gpu=8
Latex Bibtex:
None

Detailed Results

This page provides detailed results for the method(s) selected. For the first 20 test images, the percentage of erroneous pixels is depicted in the table. We use the error metric described in Sparsity Invariant CNNs (THREEDV 2017), which considers a pixel to be correctly estimated if the disparity or flow end-point error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity maps and the flow map). Underneath, the left input image, the estimated results and the error maps are shown (for disp_0/disp_1/flow/scene_flow, respectively). The error map uses the log-color scale described in Sparsity Invariant CNNs (THREEDV 2017), depicting correct estimates (<3px or <5% error) in blue and wrong estimates in red color tones. Dark regions in the error images denote the occluded pixels which fall outside the image boundaries. The false color maps of the results are scaled to the largest ground truth disparity values / flow magnitudes.

Test Set Average

iRMSE iMAE RMSE MAE
Error 2.26 0.96 848.21 237.11
This table as LaTeX

Test Image 0

iRMSE iMAE RMSE MAE
Error 2.92 0.88 979.58 248.50
This table as LaTeX

Input Image

D1 Result

D1 Error


Test Image 1

iRMSE iMAE RMSE MAE
Error 3.88 1.06 823.46 110.39
This table as LaTeX

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D1 Result

D1 Error


Test Image 2

iRMSE iMAE RMSE MAE
Error 2.21 1.50 1404.99 465.29
This table as LaTeX

Input Image

D1 Result

D1 Error


Test Image 3

iRMSE iMAE RMSE MAE
Error 3.41 1.58 673.74 246.33
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D1 Result

D1 Error


Test Image 4

iRMSE iMAE RMSE MAE
Error 2.63 1.38 568.18 218.39
This table as LaTeX

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D1 Result

D1 Error


Test Image 5

iRMSE iMAE RMSE MAE
Error 3.04 0.98 811.44 195.38
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D1 Result

D1 Error


Test Image 6

iRMSE iMAE RMSE MAE
Error 4.40 1.07 827.97 173.55
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D1 Result

D1 Error


Test Image 7

iRMSE iMAE RMSE MAE
Error 3.89 1.22 921.66 186.24
This table as LaTeX

Input Image

D1 Result

D1 Error


Test Image 8

iRMSE iMAE RMSE MAE
Error 1.67 0.66 810.58 173.63
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D1 Error


Test Image 9

iRMSE iMAE RMSE MAE
Error 2.20 1.10 878.61 232.42
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D1 Error


Test Image 10

iRMSE iMAE RMSE MAE
Error 1.94 1.34 935.62 424.07
This table as LaTeX

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D1 Result

D1 Error


Test Image 11

iRMSE iMAE RMSE MAE
Error 2.70 1.22 1394.81 471.58
This table as LaTeX

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D1 Result

D1 Error


Test Image 12

iRMSE iMAE RMSE MAE
Error 3.83 1.69 1060.95 280.57
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D1 Error


Test Image 13

iRMSE iMAE RMSE MAE
Error 1.44 0.89 749.98 231.79
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D1 Result

D1 Error


Test Image 14

iRMSE iMAE RMSE MAE
Error 1.38 0.83 645.30 189.31
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D1 Error


Test Image 15

iRMSE iMAE RMSE MAE
Error 3.47 1.28 579.43 196.31
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D1 Error


Test Image 16

iRMSE iMAE RMSE MAE
Error 1.48 0.75 602.03 197.90
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D1 Error


Test Image 17

iRMSE iMAE RMSE MAE
Error 1.42 0.68 677.02 185.35
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Test Image 18

iRMSE iMAE RMSE MAE
Error 1.72 0.97 711.49 284.04
This table as LaTeX

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D1 Result

D1 Error


Test Image 19

iRMSE iMAE RMSE MAE
Error 1.28 0.92 840.66 263.39
This table as LaTeX

Input Image

D1 Result

D1 Error




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