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loading data from /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/images!
loading calibration from /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/camera.txt!
Loading MVSNet from /home/nuc/Documents/SLAM/DenseReconstruct/tandem-master/tandem/exported/tandem/!
PHOTOMETRIC MODE WITHOUT CALIBRATION!
=============== TANDEM Settings: ===============
Setting 'dataset':
- no real-time enforcing
- 2000 active points
- 5-7 active frames
- 1-6 LM iteration each KF
- TSDF fusion: yes
- dense tracking on cpu (step=1)
- Pangolin
- Fullscreen: 0
- Mesh: 1
- Smaller Images: 1
Reading Calibration from file /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/camera.txt ... found!
found ATAN camera model, building rectifier.
Creating FOV undistorter
Input resolution: 1280 1024
In: 0.535719 0.669567 0.493249 0.500409 0.897966
Found fx=0.535719, fy=0.669567, cx=0.493249, cy=0.500409.
I'm assuming this is the "relative" calibration file format,and will rescale this by image width / height to fx=685.720714, fy=685.636463, cx=630.858138, cy=511.918472.
NO PHOTOMETRIC Calibration!
Reading Photometric Calibration from file
PhotometricUndistorter: Could not open file!
got 0 images and 0 timestamps and 0 exposures.!
ImageFolderReader: got 0 files in /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/images!
using pyramid levels 0 to 3. coarsest resolution: 80 x 60!
----DRMVSNET Initalizing fusion----
----DRMVSNET Initalizing fusion done----
DrMvsnet torch::cuda::is_vailable == true --> seems good
View Num: 7, ref index: 5
[W BinaryOps.cpp:467] Warning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (function operator())
Correctness:
Depth correct : 1, error: 0.000665128
Confidence correct: 1, error: 0.000441837
Correctness:
Depth correct : 1, error: 0.000665203
Confidence correct: 1, error: 0.000441824
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441813
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441825
Correctness:
Depth correct : 1, error: 0.000665194
Confidence correct: 1, error: 0.000441814
Correctness:
Depth correct : 1, error: 0.000665206
Confidence correct: 1, error: 0.000441811
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441811
Correctness:
Depth correct : 1, error: 0.000665191
Confidence correct: 1, error: 0.000441814
Correctness:
Depth correct : 1, error: 0.000665207
Confidence correct: 1, error: 0.00044181
Performance:
CallAsync : 15.0173 ms
Ready : 0 ms
GetResult : 224.087 ms
All looks good!
Segmentation fault (core dumped)
The text was updated successfully, but these errors were encountered:
loading data from /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/images!
loading calibration from /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/camera.txt!
Loading MVSNet from /home/nuc/Documents/SLAM/DenseReconstruct/tandem-master/tandem/exported/tandem/!
PHOTOMETRIC MODE WITHOUT CALIBRATION!
=============== TANDEM Settings: ===============
Setting 'dataset':
- no real-time enforcing
- 2000 active points
- 5-7 active frames
- 1-6 LM iteration each KF
- TSDF fusion: yes
- dense tracking on cpu (step=1)
- Pangolin
- Fullscreen: 0
- Mesh: 1
- Smaller Images: 1
Reading Calibration from file /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/camera.txt ... found!
found ATAN camera model, building rectifier.
Creating FOV undistorter
Input resolution: 1280 1024
In: 0.535719 0.669567 0.493249 0.500409 0.897966
Found fx=0.535719, fy=0.669567, cx=0.493249, cy=0.500409.
I'm assuming this is the "relative" calibration file format,and will rescale this by image width / height to fx=685.720714, fy=685.636463, cx=630.858138, cy=511.918472.
Out: Rectify Crop
Output resolution: 640 480
finding CROP optimal new model!
initial range: x: -1.1372 - 1.1901; y: -0.8311 - 0.8292!
iteration 00001: range: x: -1.1315 - 1.1841; y: -0.8311 - 0.8292!
iteration 00002: range: x: -1.1258 - 1.1782; y: -0.8311 - 0.8292!
iteration 00003: range: x: -1.1258 - 1.1782; y: -0.8270 - 0.8251!
iteration 00004: range: x: -1.1258 - 1.1782; y: -0.8228 - 0.8209!
iteration 00005: range: x: -1.1258 - 1.1782; y: -0.8228 - 0.8209!
Rectified Kamera Matrix:
277.34 0 312.234
0 291.402 239.777
0 0 1
NO PHOTOMETRIC Calibration!
Reading Photometric Calibration from file
PhotometricUndistorter: Could not open file!
got 0 images and 0 timestamps and 0 exposures.!
ImageFolderReader: got 0 files in /mnt/dataset/SLAM/TUM/TUM_dataset.zip/sequence_07/images!
using pyramid levels 0 to 3. coarsest resolution: 80 x 60!
----DRMVSNET Initalizing fusion----
----DRMVSNET Initalizing fusion done----
DrMvsnet torch::cuda::is_vailable == true --> seems good
View Num: 7, ref index: 5
[W BinaryOps.cpp:467] Warning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (function operator())
Correctness:
Depth correct : 1, error: 0.000665128
Confidence correct: 1, error: 0.000441837
Correctness:
Depth correct : 1, error: 0.000665203
Confidence correct: 1, error: 0.000441824
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441813
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441825
Correctness:
Depth correct : 1, error: 0.000665194
Confidence correct: 1, error: 0.000441814
Correctness:
Depth correct : 1, error: 0.000665206
Confidence correct: 1, error: 0.000441811
Correctness:
Depth correct : 1, error: 0.000665201
Confidence correct: 1, error: 0.000441811
Correctness:
Depth correct : 1, error: 0.000665191
Confidence correct: 1, error: 0.000441814
Correctness:
Depth correct : 1, error: 0.000665207
Confidence correct: 1, error: 0.00044181
Performance:
CallAsync : 15.0173 ms
Ready : 0 ms
GetResult : 224.087 ms
All looks good!
Segmentation fault (core dumped)
The text was updated successfully, but these errors were encountered: