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simam3d_core.py

@@ -258,3 +258,47 @@ def write_confidence_ply(
                     *point, *color, float(score), int(source_view)
                 )
             )
+
+
+def write_gaussian_ply(
+    path: str | Path,
+    points: np.ndarray,
+    colors: np.ndarray,
+    weights: np.ndarray | None = None,
+    source_views: np.ndarray | None = None,
+) -> None:
+    """Write a confidence-initialized 3DGS PLY with evidence fields."""
+    points = np.asarray(points, dtype=np.float32)
+    colors = np.asarray(colors, dtype=np.uint8)[..., :3]
+    if points.ndim != 2 or points.shape[1] != 3 or colors.shape != (len(points), 3):
+        raise ValueError("points and colors must contain matching Nx3 arrays")
+    opacity, log_scale = gaussian_initialization(weights, len(points))
+    if weights is None or len(weights) != len(points):
+        exported_confidence = np.ones(len(points), dtype=np.float32)
+    else:
+        exported_confidence = np.clip(np.asarray(weights, dtype=np.float32), 0.0, 1.0)
+    if source_views is None or len(source_views) != len(points):
+        source_views = np.full(len(points), -1, dtype=np.int32)
+    else:
+        source_views = np.asarray(source_views, dtype=np.int32)
+    dc = (colors.astype(np.float32) / 255.0 - 0.5) / 0.2820947918
+    header = [
+        "ply", "format ascii 1.0", f"element vertex {len(points)}",
+        "property float x", "property float y", "property float z",
+        "property float nx", "property float ny", "property float nz",
+        "property float f_dc_0", "property float f_dc_1", "property float f_dc_2",
+        "property float opacity", "property float scale_0", "property float scale_1",
+        "property float scale_2", "property float rot_0", "property float rot_1",
+        "property float rot_2", "property float rot_3",
+        "property float confidence", "property int source_view", "end_header",
+    ]
+    with Path(path).open("w", encoding="utf-8") as handle:
+        handle.write("\n".join(header) + "\n")
+        for point, color, alpha, scale, score, source_view in zip(
+            points, dc, opacity, log_scale, exported_confidence, source_views
+        ):
+            handle.write(
+                "{:.7g} {:.7g} {:.7g} 0 0 0 {:.7g} {:.7g} {:.7g} {:.7g} {:.7g} {:.7g} {:.7g} 1 0 0 0 {:.7g} {}\n".format(
+                    *point, *color, alpha, scale, scale, scale, float(score), int(source_view)
+                )
+            )