Command-line parameter error: unknown option -input_folder
input folder is /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/
image folder is /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/images/
p folder is /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/cams/
pmvs folder is 
numImages is 49
img_filenames is 49
Device memory used: 2100.035645MB
Device memory used: 2100.035645MB
P folder is /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/cams/
numCameras is 49
Camera size is 49
Accepted intersection angle of central rays is 10.000000 to 30.000000 degrees
Selected views: 49
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 
Reading normals and depth from disk
Size consideredIds is 49
Reading normal 0
Reading disp 0
Reading normal 1
Reading disp 1
Reading normal 2
Reading disp 2
Reading normal 3
Reading disp 3
Reading normal 4
Reading disp 4
Reading normal 5
Reading disp 5
Reading normal 6
Reading disp 6
Reading normal 7
Reading disp 7
Reading normal 8
Reading disp 8
Reading normal 9
Reading disp 9
Reading normal 10
Reading disp 10
Reading normal 11
Reading disp 11
Reading normal 12
Reading disp 12
Reading normal 13
Reading disp 13
Reading normal 14
Reading disp 14
Reading normal 15
Reading disp 15
Reading normal 16
Reading disp 16
Reading normal 17
Reading disp 17
Reading normal 18
Reading disp 18
Reading normal 19
Reading disp 19
Reading normal 20
Reading disp 20
Reading normal 21
Reading disp 21
Reading normal 22
Reading disp 22
Reading normal 23
Reading disp 23
Reading normal 24
Reading disp 24
Reading normal 25
Reading disp 25
Reading normal 26
Reading disp 26
Reading normal 27
Reading disp 27
Reading normal 28
Reading disp 28
Reading normal 29
Reading disp 29
Reading normal 30
Reading disp 30
Reading normal 31
Reading disp 31
Reading normal 32
Reading disp 32
Reading normal 33
Reading disp 33
Reading normal 34
Reading disp 34
Reading normal 35
Reading disp 35
Reading normal 36
Reading disp 36
Reading normal 37
Reading disp 37
Reading normal 38
Reading disp 38
Reading normal 39
Reading disp 39
Reading normal 40
Reading disp 40
Reading normal 41
Reading disp 41
Reading normal 42
Reading disp 42
Reading normal 43
Reading disp 43
Reading normal 44
Reading disp 44
Reading normal 45
Reading disp 45
Reading normal 46
Reading disp 46
Reading normal 47
Reading disp 47
Reading normal 48
Reading disp 48
Resizing globalstate to 49
Run cuda
Run gipuma
Grid size initrand is grid: 36-27 block: 32-32
Device memory used: 3744.202637MB
Number of iterations is 8
Blocksize is 15x15
Disparity threshold is 	0.250000
Normal threshold is 	6.283185
Number of consistent points is 	3
Cam scale is 	1.000000
Fusing points
Processing camera 0
Found 0.29 million points
Processing camera 1
Found 0.78 million points
Processing camera 2
Not enough space to save points :'(
... allocating more! :)New size of point cloud list is 1990656
Found 1.27 million points
Processing camera 3
Found 1.58 million points
Processing camera 4
Found 1.88 million points
Processing camera 5
Not enough space to save points :'(
... allocating more! :)New size of point cloud list is 3981312
Found 2.16 million points
Processing camera 6
Found 2.66 million points
Processing camera 7
Found 3.05 million points
Processing camera 8
Found 3.63 million points
Processing camera 9
Not enough space to save points :'(
... allocating more! :)New size of point cloud list is 7962624
Found 4.19 million points
Processing camera 10
Found 4.57 million points
Processing camera 11
Found 4.81 million points
Processing camera 12
Found 5.31 million points
Processing camera 13
Found 5.84 million points
Processing camera 14
Found 6.25 million points
Processing camera 15
Found 6.82 million points
Processing camera 16
Found 7.27 million points
Processing camera 17
Found 7.80 million points
Processing camera 18
Not enough space to save points :'(
... allocating more! :)New size of point cloud list is 15925248
Found 8.26 million points
Processing camera 19
Found 8.61 million points
Processing camera 20
Found 9.07 million points
Processing camera 21
Found 9.49 million points
Processing camera 22
Found 10.01 million points
Processing camera 23
Found 10.50 million points
Processing camera 24
Found 10.81 million points
Processing camera 25
Found 11.20 million points
Processing camera 26
Found 11.70 million points
Processing camera 27
Found 12.14 million points
Processing camera 28
Found 12.35 million points
Processing camera 29
Found 12.64 million points
Processing camera 30
Found 13.08 million points
Processing camera 31
Found 13.51 million points
Processing camera 32
Found 13.94 million points
Processing camera 33
Found 14.41 million points
Processing camera 34
Found 14.82 million points
Processing camera 35
Found 15.31 million points
Processing camera 36
Found 15.77 million points
Processing camera 37
Not enough space to save points :'(
... allocating more! :)New size of point cloud list is 31850496
Found 16.05 million points
Processing camera 38
Found 16.14 million points
Processing camera 39
Found 16.49 million points
Processing camera 40
Found 16.88 million points
Processing camera 41
Found 17.25 million points
Processing camera 42
Found 17.62 million points
Processing camera 43
Found 17.98 million points
Processing camera 44
Found 18.34 million points
Processing camera 45
Found 18.68 million points
Processing camera 46
Found 18.84 million points
Processing camera 47
Found 19.17 million points
Processing camera 48
Found 19.41 million points
		ELAPSED 5.397629 seconds
Writing ply file /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet//consistencyCheck-20220509-145319//final3d_model.ply
store 3D points to ply file
Not using distributed mode
netphs: [48, 32, 8]
depth_intervals_ratio: [4.0, 2.0, 1.0]
cr_base_chs: [8, 8, 8]
fea_mode: fpn
agg_mode: adaptive
depth_mode: unification
Namespace(agg_mode='adaptive', batch_size=1, blendedmvs_finetune=False, conf=[0.1, 0.15, 0.9], datapath='/home/xiaoyangg/mnt_sda95/dtu/dtu_training/mvs_training/dtu/TestRectified', dataset_name='general_eval', depth_img_save_dir='./', depth_mode='unification', depth_path=None, disp_threshold=0.25, display=False, dist_base=0.25, dist_url='env://', distributed=False, dlossw=[0.5, 1.0, 2.0], epochs=16, eval_freq=1, fea_mode='fpn', filter_method='gipuma', fix_res=False, fusibile_exe_path='/home/xiaoyangg/code/fusibile-master/build/fusibile', img_size=[512, 640], interval_ratio=[4.0, 2.0, 1.0], interval_scale=1.06, inverse_depth=False, local_rank=0, log_dir=None, lr=0.001, lr_decay=0.5, max_h=864, max_w=1152, milestones=[10, 12, 14], ndepths=[48, 32, 8], no_cuda=False, num_consistent=3.0, num_view=5, num_worker=4, numdepth=192, nviews=5, outdir='/media/sdb/xiaoyangg/unimvs_eval_results2', prob_threshold=0.3, rel_diff_base=0.0007692307692307692, resume='./unimvsnet_dtu.ckpt', save_freq=20, scheduler='steplr', start_epoch=0, summary_freq=50, sync_bn=False, test=True, testlist='datasets/lists/dtu/test.txt', testpath_single_scene=None, thres_view=5, trainlist=None, val=False, vis=False, warmup=0.2, wd=0.0)
dataset test metas: 49 interval_scale:{'scan1': 1.06}
Iter 0/49, Time:2.4369094371795654 Res:(5, 3, 864, 1152)
Iter 1/49, Time:0.31517815589904785 Res:(5, 3, 864, 1152)
Iter 2/49, Time:0.3173351287841797 Res:(5, 3, 864, 1152)
Iter 3/49, Time:0.31400632858276367 Res:(5, 3, 864, 1152)
Iter 4/49, Time:0.3169078826904297 Res:(5, 3, 864, 1152)
Iter 5/49, Time:0.32474684715270996 Res:(5, 3, 864, 1152)
Iter 6/49, Time:0.31719446182250977 Res:(5, 3, 864, 1152)
Iter 7/49, Time:0.31612610816955566 Res:(5, 3, 864, 1152)
Iter 8/49, Time:0.31523609161376953 Res:(5, 3, 864, 1152)
Iter 9/49, Time:0.3120558261871338 Res:(5, 3, 864, 1152)
Iter 10/49, Time:0.3144345283508301 Res:(5, 3, 864, 1152)
Iter 11/49, Time:0.3154926300048828 Res:(5, 3, 864, 1152)
Iter 12/49, Time:0.316631555557251 Res:(5, 3, 864, 1152)
Iter 13/49, Time:0.3157777786254883 Res:(5, 3, 864, 1152)
Iter 14/49, Time:0.31285548210144043 Res:(5, 3, 864, 1152)
Iter 15/49, Time:0.31612133979797363 Res:(5, 3, 864, 1152)
Iter 16/49, Time:0.31595730781555176 Res:(5, 3, 864, 1152)
Iter 17/49, Time:0.31764769554138184 Res:(5, 3, 864, 1152)
Iter 18/49, Time:0.31923675537109375 Res:(5, 3, 864, 1152)
Iter 19/49, Time:0.3186984062194824 Res:(5, 3, 864, 1152)
Iter 20/49, Time:0.314272403717041 Res:(5, 3, 864, 1152)
Iter 21/49, Time:0.3135809898376465 Res:(5, 3, 864, 1152)
Iter 22/49, Time:0.3150627613067627 Res:(5, 3, 864, 1152)
Iter 23/49, Time:0.313814640045166 Res:(5, 3, 864, 1152)
Iter 24/49, Time:0.3192284107208252 Res:(5, 3, 864, 1152)
Iter 25/49, Time:0.3185999393463135 Res:(5, 3, 864, 1152)
Iter 26/49, Time:0.3148961067199707 Res:(5, 3, 864, 1152)
Iter 27/49, Time:0.31452178955078125 Res:(5, 3, 864, 1152)
Iter 28/49, Time:0.3154733180999756 Res:(5, 3, 864, 1152)
Iter 29/49, Time:0.3157474994659424 Res:(5, 3, 864, 1152)
Iter 30/49, Time:0.3152918815612793 Res:(5, 3, 864, 1152)
Iter 31/49, Time:0.316908597946167 Res:(5, 3, 864, 1152)
Iter 32/49, Time:0.3186030387878418 Res:(5, 3, 864, 1152)
Iter 33/49, Time:0.3172318935394287 Res:(5, 3, 864, 1152)
Iter 34/49, Time:0.31400370597839355 Res:(5, 3, 864, 1152)
Iter 35/49, Time:0.3176746368408203 Res:(5, 3, 864, 1152)
Iter 36/49, Time:0.3174173831939697 Res:(5, 3, 864, 1152)
Iter 37/49, Time:0.3153231143951416 Res:(5, 3, 864, 1152)
Iter 38/49, Time:0.31815385818481445 Res:(5, 3, 864, 1152)
Iter 39/49, Time:0.31482958793640137 Res:(5, 3, 864, 1152)
Iter 40/49, Time:0.3143024444580078 Res:(5, 3, 864, 1152)
Iter 41/49, Time:0.3157222270965576 Res:(5, 3, 864, 1152)
Iter 42/49, Time:0.3162810802459717 Res:(5, 3, 864, 1152)
Iter 43/49, Time:0.3179645538330078 Res:(5, 3, 864, 1152)
Iter 44/49, Time:0.3144993782043457 Res:(5, 3, 864, 1152)
Iter 45/49, Time:0.312960147857666 Res:(5, 3, 864, 1152)
Iter 46/49, Time:0.31392741203308105 Res:(5, 3, 864, 1152)
Iter 47/49, Time:0.3146524429321289 Res:(5, 3, 864, 1152)
Iter 48/49, Time:0.3142709732055664 Res:(5, 3, 864, 1152)
filter depth map with probability map
Convert mvsnet output to gipuma input
Run depth map fusion & filter
/home/xiaoyangg/code/fusibile-master/build/fusibile -input_folder /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/ -p_folder /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/cams/ -images_folder /media/sdb/xiaoyangg/unimvs_eval_results2/scan1/points_mvsnet/images/ --depth_min=0.001 --depth_max=100000 --normal_thresh=360 --disp_thresh=0.25 --num_consistent=3.0