Pointnet_Pointnet2_pytorch

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in the PartNormalDataset loader it is different with the modelnetloader which uses the random sample instead of farthest_point_sample, I was very confused, can anyone help me to explain it? the ShapeNetLoader.py def _get_item(self, index): if index in self.cache: point_set, cls = self.cache[index] else: fn = self.datapath[index] cls = self.classes[self.datapath[index][0]] cls = np.array([cls]).astype(np.int32) point_set = np.loadtxt(fn[1], delimiter=',').astype(np.float32) if self.uniform: point_set = farthest_point_sample(point_set, self.npoints) else: point_set = point_set[0:self.npoints,:] point_set[:, 0:3] = pc_normalize(point_set[:, 0:3]) if not self.normal_channel: point_set = point_set[:, 0:3] if len(self.cache) < self.cache_size: self.cache[index] = (point_set, cls) return point_set, cls the ModelnetLoader.py def _get_item(self, index): if index in self.cache: point_set, cls = self.cache[index] else: fn = self.datapath[index] cls = self.classes[self.datapath[index][0]] cls = np.array([cls]).astype(np.int32) point_set = np.loadtxt(fn[1], delimiter=',').astype(np.float32) if self.uniform: point_set = farthest_point_sample(point_set, self.npoints) else: point_set = point_set[0:self.npoints,:] point_set[:, 0:3] = pc_normalize(point_set[:, 0:3]) if not self.normal_channel: point_set = point_set[:, 0:3] if len(self.cache) < self.cache_size: self.cache[index] = (point_set, cls) return point_set, cls

farthest_point_sample is used to grouping layer in SA