OpenPCDet series | 4.3 PointFeatureEncoder module analysis

Analysis of PointFeatureEncoder module

The data_dict and initialization parameters of PointFeatureEncoder in the forward function are as follows:

The point cloud feature used here is xyz+reflection intensity. The specific function is whether to use the xyz position feature as the point cloud feature for rotation. Otherwise, there is only the feature of reflection intensity in the kitti data set . data_dict['use_lead_xyz'] = TrueHowever, in the processing flow of the kitti dataset, this step does not actually bring about transformation. The only change is that this step is added to data_dict .

The implementation code of the entire class is as follows:

import numpy as np

# 点云特征编码的基类
class PointFeatureEncoder(object):
    def __init__(self, config, point_cloud_range=None):
        super().__init__()
        self.point_encoding_config = config
        assert list(self.point_encoding_config.src_feature_list[0:3]) == ['x', 'y', 'z']

        # 在pointpillars中used和src使用的特征都是4种：位置信息xyz + 点强度信息intensity
        self.used_feature_list = self.point_encoding_config.used_feature_list
        self.src_feature_list = self.point_encoding_config.src_feature_list
        self.point_cloud_range = point_cloud_range

    @property
    def num_point_features(self):
        return getattr(self, self.point_encoding_config.encoding_type)(points=None)

    def forward(self, data_dict):
        """
        Args:
            data_dict:
                points: (N, 3 + C_in)
                ...
        Returns:
            data_dict:
                points: (N, 3 + C_out),
                use_lead_xyz: whether to use xyz as point-wise features
                ...
        """
        data_dict['points'], use_lead_xyz = getattr(self, self.point_encoding_config.encoding_type)(    # (N, 4) , True
            data_dict['points']
        )
        data_dict['use_lead_xyz'] = use_lead_xyz    # True
       
        if self.point_encoding_config.get('filter_sweeps', False) and 'timestamp' in self.src_feature_list:    # False
            max_sweeps = self.point_encoding_config.max_sweeps
            idx = self.src_feature_list.index('timestamp')
            dt = np.round(data_dict['points'][:, idx], 2)
            max_dt = sorted(np.unique(dt))[min(len(np.unique(dt))-1, max_sweeps-1)]
            data_dict['points'] = data_dict['points'][dt <= max_dt]
        
        return data_dict

    def absolute_coordinates_encoding(self, points=None):
        if points is None:
            num_output_features = len(self.used_feature_list)
            return num_output_features

        assert points.shape[-1] == len(self.src_feature_list)
        point_feature_list = [points[:, 0:3]]   # 提取xyz坐标构建为一个列表元素，剩余的特征比如反射强度再同样作为一个列表元素
        for x in self.used_feature_list:
            if x in ['x', 'y', 'z']:
                continue
            idx = self.src_feature_list.index(x)    # 3
            point_feature_list.append(points[:, idx:idx+1]) # 索引特征
        point_features = np.concatenate(point_feature_list, axis=1)    # 特征进行拼接
        
        return point_features, True