add quad mesh support to usd parser

src/curobo/util/usd_helper.py

@@ -11,7 +11,7 @@
 
 # Standard Library
 import math
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Optional, Union, Tuple
 
 # Third Party
 import numpy as np
@@ -55,6 +55,44 @@ except ImportError:
         + " NOTE: Do not install this if using with ISAAC SIM."
     )
 
+# Quad mesh triangulation warp kernel
+try:
+    import warp as wp  # type: ignore
+
+    _WARP_AVAILABLE = True
+
+    @wp.kernel
+    def _triangulate_quads_kernel(
+        verts: wp.array1d(dtype=wp.vec3),
+        quads: wp.array1d(dtype=wp.vec4i),
+        tris_out: wp.array1d(dtype=wp.vec3i),
+    ):
+        ind = wp.tid()
+        ind_out = 2 * ind  # each quad as 2 triangles in the output buffer
+        quad_inds = quads[ind]
+        q0, q1, q2, q3 = quad_inds[0], quad_inds[1], quad_inds[2], quad_inds[3]
+        v0 = verts[q0]
+        v1 = verts[q1]
+        v2 = verts[q2]
+        v3 = verts[q3]
+
+        # Two possible triangulations
+        nA1 = wp.normalize(wp.cross(v1 - v0, v2 - v0))
+        nA2 = wp.normalize(wp.cross(v2 - v0, v3 - v0))
+
+        nB1 = wp.normalize(wp.cross(v3 - v1, v0 - v1))
+        nB2 = wp.normalize(wp.cross(v2 - v1, v3 - v1))
+
+        if wp.dot(nA1, nA2) > wp.dot(nB1, nB2):
+            tris_out[ind_out] = wp.vec3i(q0, q1, q2)
+            tris_out[ind_out + 1] = wp.vec3i(q0, q2, q3)
+        else:
+            tris_out[ind_out] = wp.vec3i(q1, q3, q0)
+            tris_out[ind_out + 1] = wp.vec3i(q1, q2, q3)
+
+except ImportError:  # pragma: no cover – Warp not available
+    _WARP_AVAILABLE = False
+
 
 def set_prim_translate(prim, translation):
     UsdGeom.Xformable(prim).AddTranslateOp().Set(Gf.Vec3d(translation))
@@ -348,6 +386,84 @@ def get_sphere_attrs(prim, cache=None, transform=None) -> Sphere:
     return Sphere(name=str(prim.GetPath()), pose=pose, radius=radius, position=pose[:3])
 
 
+def triangulate_mesh_faces(
+    vertices: List[List[float]],
+    faces: List[int],
+    face_count: List[int],
+) -> Tuple[List[int], List[int]]:
+    # Triangulate mesh faces. Returns a flattened index buffer and a face-count list (all 3s).
+    if not faces or not face_count:
+        raise ValueError("Empty face data provided")
+
+    expected_vertices = sum(face_count)
+    if len(faces) != expected_vertices:
+        raise ValueError(
+            f"Face data inconsistent: {len(faces)} vertices but face_count sums to {expected_vertices}"
+        )
+
+    if any(c < 3 for c in face_count):
+        raise ValueError("Found a face with < 3 vertices")
+
+    tri_count = sum(1 for c in face_count if c == 3)
+    quad_count = sum(1 for c in face_count if c == 4)
+    other_count = len(face_count) - tri_count - quad_count
+
+    log_info(f"[Triangulation] triangles={tri_count}, quads={quad_count}, others={other_count}")
+
+    if other_count == 0 and quad_count == 0:
+        return faces, face_count
+
+    new_faces: List[int] = []
+    quad_list: List[List[int]] = []
+
+    face_idx = 0
+    for count in face_count:
+        if count == 3:
+            new_faces.extend(faces[face_idx : face_idx + 3])
+        elif count == 4:
+            quad_list.append(faces[face_idx : face_idx + 4])
+        else:
+            v0 = faces[face_idx]
+            for i in range(1, count - 1):
+                new_faces.extend([v0, faces[face_idx + i], faces[face_idx + i + 1]])
+        face_idx += count
+
+    if quad_list:
+        if _WARP_AVAILABLE:
+            log_info(
+                f"[Triangulation] Using Warp GPU kernel on {len(quad_list)} quads",
+            )
+
+            import numpy as _np
+
+            verts_np = _np.asarray(vertices, dtype=_np.float32)
+            quads_np = _np.asarray(quad_list, dtype=_np.int32)
+
+            verts_wp = wp.array(verts_np, dtype=wp.vec3, device="cuda")
+            quads_wp = wp.array(quads_np, dtype=wp.vec4i, device="cuda")
+            tris_wp = wp.empty(shape=(quads_wp.shape[0] * 2,), dtype=wp.vec3i, device="cuda")
+
+            wp.launch(
+                _triangulate_quads_kernel,
+                dim=quads_wp.shape[0],
+                inputs=[verts_wp, quads_wp],
+                outputs=[tris_wp],
+            )
+
+            tris_np = tris_wp.numpy().astype(_np.int64).reshape(-1, 3)
+            new_faces.extend(tris_np.flatten().tolist())
+        else:
+            log_warn(
+                f"[Triangulation] Warp not available, CPU splitting {len(quad_list)} quads",
+            )
+            # CPU fallback – split along fixed diagonal (v0-v2)
+            for q in quad_list:
+                new_faces.extend([q[0], q[1], q[2], q[0], q[2], q[3]])
+
+    triangle_count = len(new_faces) // 3
+    return new_faces, [3] * triangle_count
+
+
 def get_mesh_attrs(prim, cache=None, transform=None) -> Mesh:
     # read cube information
     # scale = prim.GetAttribute("size").Get()
@@ -360,12 +476,12 @@ def get_mesh_attrs(prim, cache=None, transform=None) -> Mesh:
     face_count = list(prim.GetAttribute("faceVertexCounts").Get())
     # assume faces are 3:
     if len(faces) / 3 != len(face_count):
-        log_warn(
-            "Mesh faces "
-            + str(len(faces) / 3)
-            + " are not matching faceVertexCounts "
-            + str(len(face_count))
-        )
+        log_warn(f"Mesh {prim.GetPath()} has non-triangular faces, triangulating...")
+        try:
+            faces, face_count = triangulate_mesh_faces(points, faces, face_count)
+            log_info(f"Triangulated mesh {prim.GetPath()} with {len(faces)} faces")
+        except ValueError as e:
+            log_error(f"Failed to triangulate mesh {prim.GetPath()}: {e}")
             return None
     faces = np.array(faces).reshape(len(face_count), 3).tolist()
     if prim.GetAttribute("xformOp:scale").IsValid():