:param pts_rect: (N, 3) :return pts_img: (N, 2)

Args: points: (N, 3) num_sampled_points: int num_sectors: int Returns: sampled_points: (N_out, 3)

Args: roi_boxes: (M, 7 + C) points: (N, 3) Returns: sampled_points: (N_out, 3)

PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits: max(x, 0) - x * z + log(1 + exp(-abs(x))) in https://www.

PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits: max(x, 0) - x * z + log(1 + exp(-abs(x))) in https://www.

Converts an SO3 rotation matrix to Euler angles Args: so3: 3x3 rotation matrix Returns: numpy.matrixlib.defmatrix.matrix: li

Converts an SO3 rotation matrix to Euler angles Args: so3: 3x3 rotation matrix Returns: numpy.matrixlib.defmatrix.matrix: li

Converts an SO3 rotation matrix to a quaternion Args: so3: 3x3 rotation matrix Returns: numpy.ndarray: quaternion [w, x, y,

To sort the sparse features with its indices in a convenient manner. Args: features: [N, C], sparse features

Generate and split the voxels into foreground and background sparse features, based on the predicted importance values. Args:

Transforms voxel sampling grid into frustum sampling grid Args: grid: (B, X, Y, Z, 3), Voxel sampling grid gr

Undistorts an image. Args: image (:obj: `numpy.ndarray`): A distorted image. Must be demosaiced - ie. must be a 3-channel RGB ima

Undistorts an image. Args: image (:obj: `numpy.ndarray`): A distorted image. Must be demosaiced - ie. must be a 3-channel RGB ima

Args: xyz: (N, 3) xyz_batch_cnt: (batch_size) features: (N, C) new_xyz: (M, 3) ne

Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return:

:param radius: float, radius of ball :param nsample: int, maximum number of features to gather in the ball :param use_xyz:

:param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples i

:param mlp: list of int :param bn: whether to use batchnorm

Args: radius: float, radius of ball nsample: int, maximum number of features to gather in the ball

Args: query_ranges: list of int, list of neighbor ranges to group with nsamples: list of int, number of samples in ea

Args: radius: float, radius of ball nsample: int, maximum number of features to gather in the ball use_xy

Args: mlp: list of int

Args: mlp: num_voxels: max_neighbour_distance: neighbor_type: 1: ball, others: cube

Configuration files :param path: Training configuration file :param model_params: Model-specific configuration file

Args: gamma: Weighting parameter to balance loss for hard and easy examples. alpha: Weighting parameter to balance lo

Args: beta: Scalar float. L1 to L2 change point. For beta values < 1e-5, L1 loss is computed.

Args: code_weights: (#codes) float list if not None. Code-wise weights.