Method _set_polar_grid

pointersect/inference/structures.py:4013–4053

(self)

            self.cam_poses.append(poses)

    def _set_polar_grid(self):
        # both input and target are around the same circle path
        # sample num_theta points from [0,2*pi]
        # sample num_phi points from [0,pi], including two polars (0,pi)
        # assume sample theta and phi contain same number of angles on circle, 2*(num_phi-1) = num_theta
        # total point: num_theta* (num_phi-2) +2 = 2*num_phi^2 -6*num_phi +6
        # to make total point> self.n_target_imgs, num_phi >= (sqrt(2*self.n_imgs-3)+3)/2
        # common choice: self.n_target_imgs = 6, 14, 26, 42, ...
        # corresponding to num_phi = 3, 4, 5, 6,....
        assert 'min_r' in self.params
        assert 'max_r' in self.params
        max_r = self.params['max_r']
        min_r = self.params['min_r']

        assert self.n_imgs >= 3, 'polar grid sample at least 3 points'

        num_phi = int(np.ceil((np.sqrt(2 * self.n_imgs - 3) + 3.) / 2.))
        num_theta = 2 * (num_phi - 1)
        total_imgs = num_theta * (num_phi - 2) + 2

        self.cam_poses = []
        # cam_poses_neighbors: used in inverse rendering
        # neighbor camera positions to be updated
        # note that self.cam_poses_neighbors is different from self.target_cam_poses_neighbors
        # the former are used in inverse rendering, the later are used in multivew geometry
        # but their concept are the same
        self.cam_poses_neighbors = []

        for i in range(self.total):
            poses = []

            Hs_c2w, neighbor_ids = utils.generate_camera_polar_grids(
                num_phi=num_phi,
                num_theta=num_theta,
                r=max_r
            )  # (n, 4, 4)

            for j in range(self.n_imgs):
                poses.append(Hs_c2w[j])

            self.cam_poses.append(poses)

    def _set_manual(self):
        """