Fixes returned tensor for normals in TiledCamera

CONTRIBUTORS.md

@@ -150,6 +150,7 @@ Guidelines for modifications:
 * Vladimir Fokow
 * Wei Yang
 * Xavier Nal
+* Xiaodi Yuan
 * Xinjie Yao
 * Xinpeng Liu
 * Yang Jin

source/isaaclab/isaaclab/sensors/camera/tiled_camera.py

@@ -271,6 +271,11 @@ def _update_buffers_impl(self, env_ids: Sequence[int]):
             if data_type == "motion_vectors":
                 tiled_data_buffer = tiled_data_buffer[:, :, :2].contiguous()
 
+            # For normals, we only require the first three channels of the tiled buffer
+            # Note: Not doing this breaks the alignment of the data (check: https://github.com/isaac-sim/IsaacLab/issues/4239)
+            if data_type == "normals":
+                tiled_data_buffer = tiled_data_buffer[:, :, :3].contiguous()
+
             wp.launch(
                 kernel=reshape_tiled_image,
                 dim=(self._view.count, self.cfg.height, self.cfg.width),

source/isaaclab/test/sensors/test_tiled_camera.py

@@ -670,6 +670,9 @@ def test_normals_only_camera(setup_camera, device):
             assert im_data.shape == (num_cameras, camera_cfg.height, camera_cfg.width, 3)
             for i in range(4):
                 assert im_data[i].mean() > 0.0
+            # check normal norm is approximately 1
+            norms = torch.norm(im_data, dim=-1)
+            assert torch.allclose(norms, torch.ones_like(norms), atol=1e-9)
     assert camera.data.output["normals"].dtype == torch.float
     del camera