Method

SGSNet: A Lightweight Deep Complementary Network Based on Secondary Guidance, Spatial Fusion [SGSNet]
[Anonymous Submission]

Submitted on 30 Nov. 2021 14:04 by
[Anonymous Submission]

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
In this paper, we propose a lightweight depth-
completion network based on secondary guidance and
spatial fusion based on image guidance network. He
uses spatial feature extraction module and scale
feature extraction module to extract features from
different scales between and within layers in
parallel and efficiently, and generate guidance
features; and applies lightweight guidance module
to guide the extraction of LiDAR features to
obtain denser LiDAR features. Then, the LIDAR
features in different modes and RGB image features
are fused by the self-attention module; finally,
the depth information complementation module is
used to complete the complementation of the high-
modal sparse depth map and input to CSPN++ for
secondary guidance to obtain the effectively fused
dense depth map.
Parameters:
batch-size=16
Latex Bibtex:

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 2.11 0.92 723.67 209.54
This table as LaTeX

Test Image 0

iRMSE iMAE RMSE MAE
Error 2.82 0.72 836.55 166.79
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D1 Result

D1 Error


Test Image 1

iRMSE iMAE RMSE MAE
Error 2.62 0.79 739.23 82.16
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D1 Error


Test Image 2

iRMSE iMAE RMSE MAE
Error 2.02 1.42 1143.26 425.31
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Test Image 3

iRMSE iMAE RMSE MAE
Error 3.24 1.59 650.19 240.00
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Test Image 4

iRMSE iMAE RMSE MAE
Error 2.95 1.49 551.33 220.05
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Test Image 5

iRMSE iMAE RMSE MAE
Error 3.89 0.97 844.86 172.37
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Test Image 6

iRMSE iMAE RMSE MAE
Error 4.08 1.01 681.07 164.15
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Test Image 7

iRMSE iMAE RMSE MAE
Error 3.63 1.25 625.95 167.92
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Test Image 8

iRMSE iMAE RMSE MAE
Error 1.70 0.66 789.42 161.91
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Test Image 9

iRMSE iMAE RMSE MAE
Error 1.98 1.07 692.39 211.96
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Test Image 10

iRMSE iMAE RMSE MAE
Error 1.79 1.28 752.83 393.02
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Test Image 11

iRMSE iMAE RMSE MAE
Error 2.30 1.07 1190.43 409.26
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Test Image 12

iRMSE iMAE RMSE MAE
Error 3.81 1.57 1013.68 249.60
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Test Image 13

iRMSE iMAE RMSE MAE
Error 1.25 0.76 693.98 192.15
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Test Image 14

iRMSE iMAE RMSE MAE
Error 1.41 0.79 544.26 145.25
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Test Image 15

iRMSE iMAE RMSE MAE
Error 3.27 1.31 534.93 167.25
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Test Image 16

iRMSE iMAE RMSE MAE
Error 1.30 0.67 560.08 175.45
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Test Image 17

iRMSE iMAE RMSE MAE
Error 1.29 0.65 557.28 168.11
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Test Image 18

iRMSE iMAE RMSE MAE
Error 1.62 0.81 616.36 237.14
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Test Image 19

iRMSE iMAE RMSE MAE
Error 1.09 0.79 675.59 219.22
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