fixes

geometry.py

@@ -12,7 +12,7 @@ def get_intrinsics(H,W):
                      [0, f, cy],
                      [0, 0, 1]])
 
-def depth_to_points(depth, R=None, t=None):
+def depth_to_points(depth, R=None, t=None, skew=0.5):
 
     K = get_intrinsics(depth.shape[1], depth.shape[2])
     Kinv = np.linalg.inv(K)
@@ -36,7 +36,7 @@ def depth_to_points(depth, R=None, t=None):
     # coord = torch.as_tensor(coord, dtype=torch.float32, device=device)
     coord = coord[None]  # bs, h, w, 3
 
-    D = depth[:, :, :, None, None]
+    D = depth[:, :, :, None, None] * skew
     # print(D.shape, Kinv[None, None, None, ...].shape, coord[:, :, :, :, None].shape )
     pts3D_1 = D * Kinv[None, None, None, ...] @ coord[:, :, :, :, None]
     # pts3D_1 live in your coordinate system. Convert them to Py3D's

mesh.py

@@ -18,7 +18,7 @@ def depth_edges_mask(depth):
     # Compute the gradient magnitude.
     depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2)
     # Compute the edge mask.
-    mask = depth_grad > 0.05
+    mask = depth_grad > 2
     return mask
 
 
@@ -26,9 +26,9 @@ def predict_depth(model, image):
     depth = model.infer_pil(image)
     return depth
 
-def get_mesh(depth, image, keep_edges=False):
+def get_mesh(depth, image, keep_edges=False, skew=0.5):
     # limit the size of the input image
-    pts3d = depth_to_points(depth[None])
+    pts3d = depth_to_points(depth[None], skew=skew)
     pts3d = pts3d.reshape(-1, 3)
 
     # Create a trimesh mesh from the points