import torch @torch.no_grad() def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1): """ Warp kpts0 from I0 to I1 with depth, K and Rt Also check covisibility and depth consistency. Depth is consistent if relative error < 0.2 (hard-coded). Args: kpts0 (torch.Tensor): [N, L, 2] - , depth0 (torch.Tensor): [N, H, W], depth1 (torch.Tensor): [N, H, W], T_0to1 (torch.Tensor): [N, 3, 4], K0 (torch.Tensor): [N, 3, 3], K1 (torch.Tensor): [N, 3, 3], Returns: calculable_mask (torch.Tensor): [N, L] warped_keypoints0 (torch.Tensor): [N, L, 2] """ kpts0_long = kpts0.round().long() # Sample depth, get calculable_mask on depth != 0 kpts0_depth = torch.stack( [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0 ) # (N, L) nonzero_mask = kpts0_depth != 0 # Unproject kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3) kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) # Rigid Transform w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L) w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] # Project w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth # Covisible Check h, w = depth1.shape[1:3] covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \ (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1) w_kpts0_long = w_kpts0.long() w_kpts0_long[~covisible_mask, :] = 0 w_kpts0_depth = torch.stack( [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0 ) # (N, L) consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 valid_mask = nonzero_mask * covisible_mask * consistent_mask return valid_mask, w_kpts0