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ResultOfAcfNet.md

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Result of AcfNet

Model Info

  • Note: Test on GTX1080Ti, with resolution 384x1248.
Model Name FLOPS Parameters FPS Time(ms)
AcfNet(uniform) 1080.0G 5.227M 1.66 600.8
AcfNet(adaptive) 1239.0G 5.559M 1.38 723.1

Experiment

hints

  • batch size: n * m, n GPUs m batch/GPU
  • pass: clean pass or final pass of Scene Flow dataset, default clean pass
  • weight init: initialize the convolution/bn layer while training from scratch, default no initialization
  • synced bn: weather use synced bn provided by apex, default False
  • float16: weather use mixture precision training with level 01 provided by apex, default False
  • scale loss: the loss scale factor when using apex
  • time: time consuming including the training and evaluating time, in format: x h(hour) y m(minute)
  • EPE: end-point-error for SceneFlow
  • xPE (1PE, 2PE, 3PE, 5PE): pixel error where (GroundTruth - Estimation) <= x
  • D1(all): 3PE(px) & 5% for KITTI 2015

SceneFlow

10 epoch

RMSProp, lr(10 epochs) schedule: 1-10 with lr*1

model name lr batch size weight init synced bn float16 loss scale EPE(px) time BaiDuYun GoogleDrive
adaptive 0.001 4*3 ✗ ✓ ✗ ✗ 0.8308 68h18m link, pw: qxxr link
uniform 0.001 4*3 ✗ ✓ ✗ ✗ 0.8511 26h50m link, pw: 9s4e link
Disparity Predictor Ablation

If we alternate the disparity predictor from FasterSoftArgmin to LocalSoftArgmin only for reference

model name predictor 1PE 2PE 3PE 5PE EPE(px)
adaptive Faster 7.905 5.125 3.991 2.873 0.8308
adaptive Local
uniform Faster 8.626 5.544 4.291 3.061 0.8511
uniform Local 5.983 3.620 2.838 2.164 0.8216

Analysis

  1. Little difference for EPE, but make significant effect on xPE.

  2. Therefore, to get better result on KITTI, alternate the disparity predictor from FasterSoftArgmin to LocalSoftArgmin

  3. LocalSoftArgmin only works when cost volume supervised with uni-modal distribution, worse result for PSMNet

20 epoch

RMSProp, lr(20 epochs) schedule: 1-20 with lr*1

model name lr batch size weight init synced bn float16 loss scale EPE(px) time BaiDuYun GoogleDrive
adaptive 0.001 4*3 ✗ ✓ ✗ ✗ 0.7172 134h31m link, pw: qxxr link
uniform 0.001 4*3 ✗ ✓ ✗ ✗ 0.7440 56h53m link, pw: 9s4e link
Disparity Predictor Ablation

If we alternate the disparity predictor from FasterSoftArgmin to LocalSoftArgmin only for reference

model name predictor 1PE 2PE 3PE 5PE EPE(px)
adaptive Faster 6.918 4.480 3.485 2.498 0.7172
adaptive Local
uniform Faster 7.647 4.917 4.381 2.693 0.7440
uniform Local 5.338 3.232 2.536 1.927 0.7161

KITTI-2015

model name lr batch size weight init synced bn float16 loss scale D1(all) time BaiDuYun GoogleDrive
adaptive 0.001 4*3 ✗ ✓ ✗ ✗
uniform 0.001 4*3 ✗ ✓ ✗ ✗

How TO

Alternate the disparity predictor from FasterSoftArgmin to LocalSoftArgmin

In config file, change

disp_predictor=dict(
        # default FasterSoftArgmin
        type="FASTER",
        # the maximum disparity of disparity search range
        max_disp=max_disp,
        # the start disparity of disparity search range
        start_disp=0,
        # the step between near disparity sample
        dilation=1,
        # the temperature coefficient of soft argmin
        alpha=1.0,
        # whether normalize the estimated cost volume
        normalize=True,
    ),

to

disp_predictor=dict(
        # LocalSoftArgmin
        type="LOCAL",
        # the maximum disparity of disparity search range
        max_disp=max_disp,
        # the radius of window when local sampling
        radius=3,
        # the start disparity of disparity search range
        start_disp=0,
        # the step between near disparity sample
        dilation=1,
        # the step between near disparity index when local sampling
        radius_dilation=1,
        # the temperature coefficient of soft argmin
        alpha=1.0,
        # whether normalize the estimated cost volume
        normalize=True,
    ),