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Models/AdaBins

AdaBins

Reported on 26 benchmarks across 2 tasks · 1 paper · 20 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision13 results

Depth EstimationonNYU-Depth V2
Delta < 1.25· 2020-11-28
0.903
best: 0.989 (UniK3D (FT, metric))
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
Delta < 1.25^2· 2020-11-28
0.984
best: 1 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
Delta < 1.25^3· 2020-11-28
0.997
best: 1 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
absolute relative error· 2020-11-28
0.103
best: 0.026 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
Delta < 1.25· uses extra data· 2020-11-28
0.964
best: 0.99 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
Delta < 1.25^2· uses extra data· 2020-11-28
0.995
best: 0.999 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
Delta < 1.25^3· uses extra data· 2020-11-28
0.999
best: 1 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
RMSE· uses extra data· 2020-11-28
2.36
best: 1.394 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
RMSE log· uses extra data· 2020-11-28
0.088
best: 0.048 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonKITTI Eigen split
absolute relative error· uses extra data· 2020-11-28
0.058
best: 0.029 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
RMS· 2020-11-28
0.364
best: 0.792 (PAD-Net)
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
RMSE· 2020-11-28
0.364
best: 0.013 (Defocus/DepthNet (Normalized))
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
Depth EstimationonNYU-Depth V2
log 10· 2020-11-28
0.044
best: 0.059 (SC-DepthV2)
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141

Methodology13 results

3DonNYU-Depth V2
Delta < 1.25· 2020-11-28
0.903
best: 0.989 (UniK3D (FT, metric))
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
Delta < 1.25^2· 2020-11-28
0.984
best: 1 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
Delta < 1.25^3· 2020-11-28
0.997
best: 1 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
absolute relative error· 2020-11-28
0.103
best: 0.026 (HybridDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
Delta < 1.25· uses extra data· 2020-11-28
0.964
best: 0.99 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
Delta < 1.25^2· uses extra data· 2020-11-28
0.995
best: 0.999 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
Delta < 1.25^3· uses extra data· 2020-11-28
0.999
best: 1 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
RMSE· uses extra data· 2020-11-28
2.36
best: 1.394 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
RMSE log· uses extra data· 2020-11-28
0.088
best: 0.048 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonKITTI Eigen split
absolute relative error· uses extra data· 2020-11-28
0.058
best: 0.029 (SPIDepth)
SOTA
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
RMS· 2020-11-28
0.364
best: 0.792 (PAD-Net)
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
RMSE· 2020-11-28
0.364
best: 0.013 (Defocus/DepthNet (Normalized))
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141
3DonNYU-Depth V2
log 10· 2020-11-28
0.044
best: 0.059 (SC-DepthV2)
AdaBins: Depth Estimation using Adaptive Bins arXiv:2011.14141