pvrcnn在openpcdet框架下的实现流程

1.数据预处理

pcdet/datasets/kitti/kitti_dataset.py下__getitem__函数

    def __getitem__(self, index):
        # index = 4
        if self._merge_all_iters_to_one_epoch:
            index = index % len(self.kitti_infos)

        info = copy.deepcopy(self.kitti_infos[index])

        sample_idx = info['point_cloud']['lidar_idx']
        img_shape = info['image']['image_shape']
        calib = self.get_calib(sample_idx)
        get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])

        input_dict = {
   
            'frame_id': sample_idx,
            'calib': calib,
        }

        if 'annos' in info:
            annos = info['annos']
            annos = common_utils.drop_info_with_name(annos, name='DontCare')
            loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
            gt_names = annos['name']
            gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
            gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)

            input_dict.update({
   
                'gt_names': gt_names,
                'gt_boxes': gt_boxes_lidar
            })
            if "gt_boxes2d" in get_item_list:
                input_dict['gt_boxes2d'] = annos["bbox"]

            road_plane = self.get_road_plane(sample_idx)
            if road_plane is not None:
                input_dict['road_plane'] = road_plane

        if "points" in get_item_list:
            points = self.get_lidar(sample_idx)
            '''
            1.将点云数据从激光雷达坐标系转换为相机坐标系中的矩形坐标
            2.表示只保留视场（Field of View，FOV）内的点云数据，即位于相机视野中的数据
            '''
            if self.dataset_cfg.FOV_POINTS_ONLY:
                pts_rect = calib.lidar_to_rect(points[:, 0:3])
                fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
                points = points[fov_flag]
            input_dict['points'] = points

        if "images" in get_item_list:
            input_dict['images'] = self.get_image(sample_idx)

        if "depth_maps" in get_item_list:
            input_dict['depth_maps'] = self.get_depth_map(sample_idx)

        if "calib_matricies" in get_item_list:
            input_dict["trans_lidar_to_cam"], input_dict["trans_cam_to_img"] = kitti_utils.calib_to_matricies(calib)

        input_dict['calib'] = calib
        

此时input_dict为当前索引对应的信息，最重要的是FOV视角内的点云数据和点云形式的标注框形式

        data_dict = self.prepare_data(data_dict=input_dict)

        data_dict['image_shape'] = img_shape
        return data_dict
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其中prepare_data函数中对数据主要做了三个操作（yaml中配置）

• 数据增强（真值框采样、随机全局翻转、旋转、缩放等）
• 通道维数选择（从输入的维度中选择需要的维度作为输出，在此代码中输出维数=输入维数）
• 数据处理（去除视野范围外的点、打乱点的顺序，将点云转换成体素）

2.生成anchor

pcdet/models/dense_heads/target_assigner/anchor_generator.py下的generate_anchors函数

    def generate_anchors(self, grid_sizes):
        assert len(grid_sizes) == self.num_of_anchor_sets
        # 1.初始化
        all_anchors = []
        num_anchors_per_location = []
        # 2.三个类别的先验框逐类别生成
        for grid_size, anchor_size, anchor_rotation, anchor_height, align_center in zip(
                grid_sizes, self.anchor_sizes, self.anchor_rotations, self.anchor_heights, self.align_center):
            # 每个位置产生2个anchor，这里的2代表两个方向
            # 2.三个类别的先验框逐类别生成
            num_anchors_per_location.append(len(anchor_rotation) * len(anchor_size) * len(anchor_height))
            if align_center:
                x_stride = (self.anchor_range[3] - self.anchor_range[0]) / grid_size[0]
                y_stride = (self.anchor_range[4] - self.anchor_range[1]) / grid_size[1]
                x_offset, y_offset = x_stride / 2, y_stride / 2
            else:
                # 计算每个网格的在点云空间中的实际大小,将每个anchor映射回实际点云
                x_stride = (self.anchor_range[3] - self.anchor_range[0]) / (grid_size[0] - 1)
                y_stride = (self.anchor_range[4] - self.anchor_range[1
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