first commit

README.md

@@ -41,10 +41,12 @@ Later run
 
 ```
 cd nvdiffrec
-python eval.py --config $DMTET_CONFIG --sample-path $SAMPLE_PATH --deform-scale $DEFORM_SCALE
+python eval.py --config $DMTET_CONFIG --sample-path $SAMPLE_PATH --deform-scale $DEFORM_SCALE [--angle_index $ANGLE_INDEX]
 ```
 
-where `$SAMPLE_PATH` is the generated sample `.npy` file in `$OUTPUT_PATH`, and `$DEFORM_SCALE` is the scale of deformation of tet vertices set for the DMTet dataset (we use 3.0 for resolution 64 as default; change the value for your own datasets).
+where `$SAMPLE_PATH` is the generated sample `.npy` file in `$OUTPUT_PATH`, and `$DEFORM_SCALE` is the scale of deformation of tet vertices set for the DMTet dataset (we use 3.0 for resolution 64 as default; change the value for your own datasets). Change `$ANGLE_INDEX` to some number from 0 to 50 if images rendered from different angles are desired.
+
+A mesh file (`.obj`) will also be saved to the folder, which can be viewed in tools such as MeshLab.
 
 
 ### Single-view Conditional Generation
@@ -80,6 +82,8 @@ python eval.py --config $DMTET_CONFIG --sample-path $SAMPLE_PATH  --deform-scale
 
 Caution: the deformation scale should be consistent for single view fitting and the diffusion model. Check before you run conditional generation.
 
+
+
 ## Training
 
 For ShapeNet, first create a list of paths of all ground-truth meshes and store them as a json file under `./nvdiffrec/data/shapenet_json`.

nvdiffrec/eval.py

@@ -321,6 +321,7 @@ if __name__ == "__main__":
     parser.add_argument('-vn', '--viz-name', type=str, default='viz')
     parser.add_argument('--unnormalized_sdf', action="store_true")
     parser.add_argument('--validate', type=bool, default=True)
+    parser.add_argument('--angle-index', type=int, default=25)
     
     FLAGS = parser.parse_args()
 
@@ -430,25 +431,25 @@ if __name__ == "__main__":
         
         
         ### save image (before post-processing)
-        v_pose = rotate_scene(FLAGS, 25) ## pick a pose (pose # from 0 to 50)
+        v_pose = rotate_scene(FLAGS, FLAGS.angle_index) ## pick a pose (pose # from 0 to 50)
         result_image, _ = validate_itr(glctx, prepare_batch(v_pose, FLAGS.background), geometry, opt_material, lgt, FLAGS)
         result_image = result_image.detach().cpu().numpy()
         util.save_image(os.path.join(viz_path, ('%s_%06d.png' % (FLAGS.viz_name, no_data))), result_image)
 
 
-        # ### save post-processed mesh
-        # mesh_savepath = os.path.join(mesh_path, '{:06d}.obj'.format(no_data))
-        # save_obj(
-        #     verts=base_mesh.v_pos,
-        #     faces=base_mesh.t_pos_idx,
-        #     f=mesh_savepath
-        # )
+        ### save post-processed mesh
+        mesh_savepath = os.path.join(mesh_path, '{:06d}.obj'.format(no_data))
+        save_obj(
+            verts=base_mesh.v_pos,
+            faces=base_mesh.t_pos_idx,
+            f=mesh_savepath
+        )
 
-        # ms = pymeshlab.MeshSet()
-        # ms.load_new_mesh(mesh_savepath)
-        # ms.meshing_isotropic_explicit_remeshing()
-        # ms.apply_coord_laplacian_smoothing(stepsmoothnum=3, cotangentweight=False)
-        # # ms.apply_coord_laplacian_smoothing(stepsmoothnum=3, cotangentweight=True) ## for smoother surface
-        # ms.meshing_isotropic_explicit_remeshing()
-        # ms.apply_filter_script()
-        # ms.save_current_mesh(mesh_savepath)
+        ms = pymeshlab.MeshSet()
+        ms.load_new_mesh(mesh_savepath)
+        ms.meshing_isotropic_explicit_remeshing()
+        ms.apply_coord_laplacian_smoothing(stepsmoothnum=3, cotangentweight=False)
+        # ms.apply_coord_laplacian_smoothing(stepsmoothnum=3, cotangentweight=True) ## for smoother surface
+        ms.meshing_isotropic_explicit_remeshing()
+        ms.apply_filter_script()
+        ms.save_current_mesh(mesh_savepath)