Open fariba87 opened 1 month ago
Based on your description, I am curious to know if the sparse reconstruction process in COLMAP was successful. Could you possibly share the sparse point cloud and your input images for further analysis?
Based on your description, I am curious to know if the sparse reconstruction process in COLMAP was successful. Could you possibly share the sparse point cloud and your input images for further analysis?
I did two experiment. 1) without applying mask-> colmap tried to find points on background as well in sparse reconstruction step 2) with applying mask in feature extraction step of COLMAP -> colmap just found the sparse points on the object. likewise, i can see the size of point3d.bin file in the later one is smaller. if i plot those points via matplotlib they are also only limited to my object. my question is regarding depth_gt and depth_min and depth_max. how should they correctly be specified?
Sorry, I haven't tried agisoft metashape before. Maybe it's better to use depthmin and depthmax in cam.txt files from colmap .
I appreciate your great work and i have some question I want to train the model on my custom data. here is my steps: 1) i captured some pictures while moving around a stationary object by iphone 14 2) i use foreground masks in feature extraction process of COLMAP to restrict the sparse point cloud to foreground object 3) i do refine bundle adjustment to refine focal length and principle point 4) i bitwise_and image and masks so the inputs are also masked images(without Background) 5) i use colmap2mvsnet to create cam.txt and pair.txt 6) for ground truth depthmap, i used rendered depthmap from agisoft metashape(gray image[0-255], then i use minmaxscaler from sklearn to scale it between [depthmin and depthmax] per image(resulted from cam.txt files)