QueryAndGroup

class mmcv.ops.QueryAndGroup(max_radius: float, sample_num: int, min_radius: float = 0.0, use_xyz: bool = True, return_grouped_xyz: bool = False, normalize_xyz: bool = False, uniform_sample: bool = False, return_unique_cnt: bool = False, return_grouped_idx: bool = False)

Groups points with a ball query of radius.

Parameters

• max_radius (float) – The maximum radius of the balls. If None is given, we will use kNN sampling instead of ball query.

• sample_num (int) – Maximum number of features to gather in the ball.

• min_radius (float, optional) – The minimum radius of the balls. Default: 0.

• use_xyz (bool, optional) – Whether to use xyz. Default: True.

• return_grouped_xyz (bool, optional) – Whether to return grouped xyz. Default: False.

• normalize_xyz (bool, optional) – Whether to normalize xyz. Default: False.

• uniform_sample (bool, optional) – Whether to sample uniformly. Default: False

• return_unique_cnt (bool, optional) – Whether to return the count of unique samples. Default: False.

• return_grouped_idx (bool, optional) – Whether to return grouped idx. Default: False.

forward(points_xyz: torch.Tensor, center_xyz: torch.Tensor, features: Optional[torch.Tensor] = None) → Union[torch.Tensor, Tuple]

Parameters

• points_xyz (torch.Tensor) – (B, N, 3) xyz coordinates of the points.

• center_xyz (torch.Tensor) – (B, npoint, 3) coordinates of the centriods.

• features (torch.Tensor) – (B, C, N) The features of grouped points.

Returns

(B, 3 + C, npoint, sample_num) Grouped concatenated coordinates and features of points.

Return type

Tuple | torch.Tensor