Reproducing results with SuperPoint + MNN on Megadepth-1500 about lightglue HOT 2 CLOSED

def estimate_relative_pose(
    kpts0, kpts1, K0, K1, thresh, conf=0.99999, solver=cv2.RANSAC
):
    if len(kpts0) < 5:
        return None

    f_mean = np.mean([K0[0, 0], K1[0, 0], K0[1, 1], K1[1, 1]])
    norm_thresh = thresh / f_mean

    kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None]
    kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None]

    E, mask = cv2.findEssentialMat(
        kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf, method=solver
    )

    if E is None:
        return None

    best_num_inliers = 0
    ret = None
    for _E in np.split(E, len(E) / 3):
        n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
        if n > best_num_inliers:
            best_num_inliers = n
            ret = (R, t[:, 0], mask.ravel() > 0)
    return ret

import poselib


def intrinsics_to_camera(K):
    px, py = K[0, 2], K[1, 2]
    fx, fy = K[0, 0], K[1, 1]
    return {
        "model": "PINHOLE",
        "width": int(2 * px),
        "height": int(2 * py),
        "params": [fx, fy, px, py],
    }


M, info = poselib.estimate_relative_pose(
    kpts0, kpts1,
    intrinsics_to_camera(K0),
    intrinsics_to_camera(K1),
    {"max_epipolar_error": th},
)

R, t, inl = M.R, M.t, info["inliers"]