fix: viser_ply filtrage outliers statistiques — supprime gros pâtés bruités

scripts/viser_ply.py

@@ -1,15 +1,7 @@
 #!/usr/bin/env python3
-"""Minimal standalone viser viewer for a single PLY point cloud.
-
-Usage:
-    python3 viser_ply.py path/to/cloud.ply --port 8200
-"""
+"""Minimal standalone viser viewer for a single PLY point cloud."""
 from __future__ import annotations
-
-import argparse
-import sys
-import time
-
+import argparse, sys, time
 import numpy as np
 
 
@@ -18,10 +10,9 @@ def main():
     ap.add_argument("ply", help="PLY file to visualize")
     ap.add_argument("--port", type=int, default=8200)
     ap.add_argument("--point-size", type=float, default=0.01)
-    ap.add_argument("--downsample", type=float, default=0.0,
-                    help="Voxel downsample size (0 = no downsample)")
-    ap.add_argument("--max-points", type=int, default=2_000_000,
-                    help="Random subsample to this many points if cloud is larger")
+    ap.add_argument("--downsample", type=float, default=0.0)
+    ap.add_argument("--max-points", type=int, default=2_000_000)
+    ap.add_argument("--no-filter", action="store_true", help="Disable outlier removal")
     args = ap.parse_args()
 
     try:
@@ -31,15 +22,30 @@ def main():
         sys.exit(f"missing dep: {e}")
 
     pcd = o3d.io.read_point_cloud(args.ply)
+    n_raw = len(pcd.points)
+    print(f"loaded {n_raw:,} pts from {args.ply}", flush=True)
+
     if args.downsample > 0:
         pcd = pcd.voxel_down_sample(args.downsample)
+        print(f"after voxel downsample: {len(pcd.points):,} pts", flush=True)
+
+    if not args.no_filter and len(pcd.points) > 100:
+        # Remove statistical outliers (blobs / noise from low-confidence frames)
+        pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+        print(f"after outlier removal: {len(pcd.points):,} pts", flush=True)
+        # Second pass tighter for dense clouds
+        if len(pcd.points) > 200_000:
+            pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=30, std_ratio=1.5)
+            print(f"after 2nd pass: {len(pcd.points):,} pts", flush=True)
+
     pts = np.asarray(pcd.points, dtype=np.float32)
     cols = np.asarray(pcd.colors, dtype=np.float32) if pcd.has_colors() else np.ones_like(pts) * 0.7
+
     if len(pts) > args.max_points:
         idx = np.random.choice(len(pts), args.max_points, replace=False)
         pts, cols = pts[idx], cols[idx]
-    print(f"loaded {len(pts):,} pts from {args.ply}", flush=True)
 
+    print(f"displaying {len(pts):,} pts", flush=True)
     server = viser.ViserServer(host="0.0.0.0", port=args.port)
     server.scene.add_point_cloud(
         "/cloud", points=pts, colors=(cols * 255).astype(np.uint8), point_size=args.point_size