The repository contains the implementation of our ECCV 2022 paper: CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene.
Hello again.
I tested the network with your pretrained models by running run_matterport.py with these settings:
checkpoint = "pretrain" # experiment_name, sys.argv[1]
c_num = 100000 # yours is c_num = 100000.
model, args_model = net_util.load_origin_unet_model(f"pre-trained-weight/{checkpoint}/hyper_small.json",c_num,use_nerf= False,layer = 5)
..............
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data_dir = "/media/data2/data/Matterport3d/datav1/matterport_noise_result/" # contains noisy point cloud.
scene = "2t7WUuJeko7"
region = "region0"#
I tested with your pretrained model. the mesh results look like the following:
Do you know why it contains the patterns? Do you have any idea how to remove it ?
Thanks a lot for your time !
Regards
Hello again. I tested the network with your pretrained models by running run_matterport.py with these settings: checkpoint = "pretrain" # experiment_name, sys.argv[1] c_num = 100000 # yours is c_num = 100000. model, args_model = net_util.load_origin_unet_model(f"pre-trained-weight/{checkpoint}/hyper_small.json",c_num,use_nerf= False,layer = 5) .............. .............. data_dir = "/media/data2/data/Matterport3d/datav1/matterport_noise_result/" # contains noisy point cloud. scene = "2t7WUuJeko7" region = "region0"#
I tested with your pretrained model. the mesh results look like the following:
Do you know why it contains the patterns? Do you have any idea how to remove it ? Thanks a lot for your time ! Regards