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alr-internship
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self-supervised-depth-denoising
Denoising YCB Objects with a self-supervised deep neural network
MIT License
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Check if 3D to 2D transformation correct
#24
dapetri
closed
2 years ago
1
Edge curvature in trained network predictions
#23
claudius-kienle
closed
2 years ago
1
evaluate different loss types
#22
dapetri
closed
2 years ago
2
split region mask and nan mask
#21
claudius-kienle
opened
2 years ago
1
skip connections
#20
claudius-kienle
closed
2 years ago
2
evaluate model with output activation
#19
claudius-kienle
closed
2 years ago
3
resize input to fill input tensor
#18
claudius-kienle
opened
2 years ago
0
remove pixel in calibrated images pairs that are too apart
#17
dapetri
closed
2 years ago
2
remove dataset outliers
#16
claudius-kienle
closed
2 years ago
4
depth values may be in meter instead of millimeter
#15
claudius-kienle
closed
2 years ago
0
JSON for test/train/val split
#14
claudius-kienle
closed
2 years ago
0
mask ycb objects to focus network denoising on relevant part
#13
claudius-kienle
closed
2 years ago
4
can NaNs be inferred in a network
#12
claudius-kienle
opened
2 years ago
0
remove NaNs from RGB and depth images
#11
claudius-kienle
closed
2 years ago
1
add notebook to visualize inference of trained models.
#10
claudius-kienle
closed
2 years ago
1
calibrate dataset
#9
claudius-kienle
closed
2 years ago
1
apply augmentation to dataset at training time
#8
claudius-kienle
closed
2 years ago
1
check input/ouptut datatypes
#7
claudius-kienle
opened
2 years ago
0
check dataset for 'bad' samples
#6
claudius-kienle
closed
2 years ago
1
Check tensor datatypes. Looks like some depth maps have uint16. Is that oke?
#5
claudius-kienle
closed
2 years ago
1
evaluate network with activated augmentation
#4
claudius-kienle
closed
2 years ago
1
generate augmented dataset by applying augmentation in 3d space
#3
claudius-kienle
closed
2 years ago
1
normalize depth inputs
#2
claudius-kienle
opened
2 years ago
1
output activation function
#1
claudius-kienle
closed
2 years ago
1