Add two-video drone 3DGS pipeline with Apple Silicon fixes

- main.py: extract frames from two videos, run COLMAP feature extraction
- match_features.py: Python-based within-video SIFT matching via OpenCV
  (replaces colmap exhaustive_matcher which segfaults on ARM64 in COLMAP 4.x)
- match_crossvideo.py: exhaustive cross-video matching (v1×v2) to stitch
  two flights into a single COLMAP model
- run.sh: entry point for frame extraction + feature extraction
- train_splat.sh: ns-process-data → splatfacto → .ply export, with
  correct PATH for Homebrew ffmpeg and MPS device flags for Apple Silicon
- .gitignore: exclude videos, generated scene data, venv, logs
- README.md: full pipeline walkthrough, all known issues and fixes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

match_crossvideo.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Cross-video exhaustive matching for two-flight drone footage.
+
+Matches every v1_* frame against every v2_* frame. The within-video
+matches from match_features.py are already in the database and are not
+touched. After this script, re-run colmap mapper to stitch the scene.
+"""
+import argparse
+import sqlite3
+import numpy as np
+import cv2
+from pathlib import Path
+
+MIN_INLIERS = 15
+RATIO_TEST  = 0.75
+RANSAC_ERROR = 4.0
+KMAX = 2_147_483_647
+
+
+def pair_id(id1: int, id2: int) -> int:
+    lo, hi = (id1, id2) if id1 < id2 else (id2, id1)
+    return KMAX * lo + hi
+
+
+def load_desc_kpts(cur, image_id):
+    cur.execute("SELECT rows, cols, data FROM descriptors WHERE image_id=?", (image_id,))
+    r = cur.fetchone()
+    desc = np.frombuffer(r[2], dtype=np.uint8).reshape(r[0], r[1]) if r else np.zeros((0,128), dtype=np.uint8)
+
+    cur.execute("SELECT rows, cols, data FROM keypoints WHERE image_id=?", (image_id,))
+    r = cur.fetchone()
+    if r:
+        kp = np.frombuffer(r[2], dtype=np.float32).reshape(r[0], r[1])
+        kpts = kp[:, :2]
+    else:
+        kpts = np.zeros((0, 2), dtype=np.float32)
+    return desc, kpts
+
+
+def match_pair(desc1, desc2, kp1, kp2):
+    if len(desc1) < 8 or len(desc2) < 8:
+        return None, None
+    bf  = cv2.BFMatcher(cv2.NORM_L2)
+    raw = bf.knnMatch(desc1.astype(np.float32), desc2.astype(np.float32), k=2)
+    good = []
+    for m_pair in raw:
+        if len(m_pair) == 2:
+            m, n = m_pair
+            if m.distance < RATIO_TEST * n.distance:
+                good.append((m.queryIdx, m.trainIdx))
+    if len(good) < MIN_INLIERS:
+        return None, None
+    arr  = np.array(good, dtype=np.uint32)
+    pts1 = kp1[arr[:, 0]]
+    pts2 = kp2[arr[:, 1]]
+    F, mask = cv2.findFundamentalMat(
+        pts1, pts2, cv2.FM_RANSAC,
+        ransacReprojThreshold=RANSAC_ERROR,
+        confidence=0.9999, maxIters=2000,
+    )
+    if F is None or mask is None:
+        return None, None
+    inliers = arr[mask.ravel().astype(bool)]
+    return (inliers, F) if len(inliers) >= MIN_INLIERS else (None, None)
+
+
+def write_pair(cur, pid, inliers, F):
+    blob   = inliers.astype(np.uint32).tobytes()
+    z9     = np.zeros(9, dtype=np.float64).tobytes()
+    z4     = np.zeros(4, dtype=np.float64).tobytes()
+    z3     = np.zeros(3, dtype=np.float64).tobytes()
+    F_blob = F.flatten().astype(np.float64).tobytes()
+    cur.execute(
+        "INSERT OR REPLACE INTO matches (pair_id, rows, cols, data) VALUES (?,?,?,?)",
+        (pid, len(inliers), 2, blob),
+    )
+    cur.execute(
+        "INSERT OR REPLACE INTO two_view_geometries "
+        "(pair_id, rows, cols, data, config, F, E, H, qvec, tvec) VALUES (?,?,?,?,?,?,?,?,?,?)",
+        (pid, len(inliers), 2, blob, 3, F_blob, z9, z9, z4, z3),
+    )
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--db", default="my_scene/database.db")
+    args = p.parse_args()
+
+    db  = sqlite3.connect(args.db)
+    db.execute("PRAGMA journal_mode=WAL")
+    cur = db.cursor()
+
+    cur.execute("SELECT image_id, name FROM images ORDER BY name")
+    rows = cur.fetchall()
+    v1 = [(id, name) for id, name in rows if name.startswith("v1_")]
+    v2 = [(id, name) for id, name in rows if name.startswith("v2_")]
+    total_pairs = len(v1) * len(v2)
+    print(f"v1={len(v1)} frames  v2={len(v2)} frames  cross-pairs={total_pairs}")
+
+    # Preload all v1 and v2 descriptors into RAM
+    print("Loading v1 descriptors…")
+    v1_data = {id: load_desc_kpts(cur, id) for id, _ in v1}
+    print("Loading v2 descriptors…")
+    v2_data = {id: load_desc_kpts(cur, id) for id, _ in v2}
+
+    matched = skipped = i = 0
+    for id1, _ in v1:
+        desc1, kp1 = v1_data[id1]
+        for id2, _ in v2:
+            desc2, kp2 = v2_data[id2]
+            inliers, F = match_pair(desc1, desc2, kp1, kp2)
+            if inliers is not None:
+                write_pair(cur, pair_id(id1, id2), inliers, F)
+                matched += 1
+            else:
+                skipped += 1
+            i += 1
+            if i % 500 == 0:
+                pct = 100 * i / total_pairs
+                print(f"  [{i}/{total_pairs} {pct:.0f}%] cross-matched={matched}", flush=True)
+                db.commit()
+
+    db.commit()
+    db.close()
+    print(f"\nDone. {matched} cross-video pairs matched, {skipped} below threshold.")
+    print("Now delete my_scene/sparse/* and re-run colmap mapper.")
+
+
+if __name__ == "__main__":
+    main()