what's the meaning of the numbers and can we change the parameters in this function?

[ZeweiXu]

This function is in scannet_dataset.py, I guess it is used to do coordinates normalization. What are the meaning of those numbers (e.g. 1.5,3.0,31.0,62.0)? And the 10 in the for loop?
Any help would be greatly appreciated!

def __getitem__(self, index):
    import numpy as np
    point_set = self.scene_points_list[index]
    semantic_seg = self.semantic_labels_list[index].astype(np.int32)
    coordmax = np.max(point_set,axis=0)
    coordmin = np.min(point_set,axis=0)
    smpmin = np.maximum(coordmax-[1.5,1.5,3.0], coordmin)
    smpmin[2] = coordmin[2]
    smpsz = np.minimum(coordmax-smpmin, [1.5, 1.5, 3.0)
    smpsz[2] = coordmax[2]-coordmin[2]
    cur_semantic_seg = None
    cur_point_set = None
    mask = None
    for i in range(10):
        curcenter = point_set[np.random.choice(len(semantic_seg),1)[0],:]
        curmin = curcenter-[0.75, 0.75, 1.5]
        curmax = curcenter+[0.75, 0.75, 1.5]
        curmin[2] = coordmin[2]
        curmax[2] = coordmax[2]
        curchoice = np.sum((point_set >= (curmin-0.2))*(point_set <= (curmax+0.2)),axis=1) == 3
        cur_point_set = point_set[curchoice, :]
        cur_semantic_seg = semantic_seg[curchoice]
        if len(cur_semantic_seg) == 0:
            continue
        mask = np.sum((cur_point_set >= (curmin-0.01))*(cur_point_set <= (curmax+0.01)), axis=1) == 3
        vidx = np.ceil((cur_point_set[mask, :]-curmin)/(curmax-curmin)*[31.0, 31.0, 62.0])
        vidx = np.unique(vidx[:, 0] * 31.0 * 62.0 + vidx[:, 1] * 62.0 + vidx[:, 2])
        isvalid = np.sum(cur_semantic_seg > 0)/len(cur_semantic_seg) >= 0.7 and len(vidx)/31.0/31.0/62.0 >= 0.02
        if isvalid:
            break
    choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True)
    point_set = cur_point_set[choice,:]

    semantic_seg = cur_semantic_seg[choice]
    mask = mask[choice]
    sample_weight = self.labelweights[semantic_seg]
    return point_set, semantic_seg, sample_weight

[ouceduxzk]

its not for coord normalization, its about point clouds selection, the idea is to through away as much more unnecessary point clouds as possible without know about what is the content of the point clouds