Log space blending method

configs/boeing_airplane/config.yaml

@@ -0,0 +1,30 @@
+bound_method: "forward"
+render_method: "splatfacto"
+case_name: "boeing_airplane"
+odd_type: "cylinder"
+save_filename: null
+debug: true
+
+width: 256
+height: 256
+fx: 216
+fy: 216
+eps2d: 8.0
+
+downsampling_ratio: 2
+tile_size_abstract: 8
+tile_size_render: 24
+min_distance: 0.01
+max_distance: 100.0
+gs_batch: 20
+part: [1, 2, 2]
+
+data_time: "2026-01-31_235019"
+checkpoint_filename: "step-000299999.ckpt"
+
+bg_img_path: null
+bg_pure_color: [0.0, 1.0, 0.0]
+
+save_ref: true
+save_bound: true
+N_samples: 5

configs/boeing_airplane/samples.json

@@ -0,0 +1,14 @@
+[
+    {
+        "index": 0,
+        "pose": [
+            3.096113,
+            0.375645,
+            3.329813,
+            -3.141593,
+            0.785398,
+            1.570796
+        ],
+        "radius": 0.1
+    }
+]

configs/boeing_airplane/traj.json

@@ -0,0 +1,38 @@
+[
+    {
+        "index": 0,
+        "pose": [
+            3.096113,
+            0.375645,
+            3.329813,
+            -3.141593,
+            0.785398,
+            1.570796
+        ],
+        "tangent": [
+            0.0,
+            -1.0,
+            0.0
+        ],
+        "gate": null,
+        "radius": 0.1
+    },
+    {
+        "index": 1,
+        "pose": [
+            3.096113,
+            -0.624355,
+            3.329813,
+            -3.141593,
+            0.785398,
+            1.570796
+        ],
+        "tangent": [
+            0.0,
+            -1.0,
+            0.0
+        ],
+        "gate": null,
+        "radius": 0.1
+    }
+]

scripts/abstract_gsplat.py

@@ -73,7 +73,7 @@ def main(setup_dict):
         os.makedirs(save_folder_full)
 
     # Load Trained 3DGS 
-    scene_parameters = torch.load(checkpoint_file, weights_only=False)
+    scene_parameters = torch.load(checkpoint_file, weights_only=False, map_location=DEVICE)
     means = scene_parameters['pipeline']['_model.gauss_params.means'].to(DEVICE)
     quats = scene_parameters['pipeline']['_model.gauss_params.quats'].to(DEVICE)
     opacities = torch.sigmoid(scene_parameters['pipeline']['_model.gauss_params.opacities']).to(DEVICE)
@@ -160,6 +160,11 @@ def main(setup_dict):
             if save_bound:
                 img_lb = np.zeros((height, width,3))
                 img_ub = np.zeros((height, width,3))
+                input_dim = input_center.shape[-1]
+                img_lA = torch.zeros(height, width, 3, input_dim, device=DEVICE)
+                img_uA = torch.zeros(height, width, 3, input_dim, device=DEVICE)
+                img_lbias = torch.zeros(height, width, 3, device=DEVICE)
+                img_ubias = torch.zeros(height, width, 3, device=DEVICE)
 
             # Create Tiles Queue
             tiles_queue = [
@@ -188,11 +193,15 @@ def main(setup_dict):
                     img_ref[hl:hu, wl:wu, :] = ref_tile_np
 
                 if save_bound:
-                    lb_tile, ub_tile = alpha_blending_ptb(verf_net, input_center, input_lb, input_ub, bound_method)
+                    lb_tile, ub_tile, lA_tile, uA_tile, lbias_tile, ubias_tile = alpha_blending_ptb(verf_net, input_center, input_lb, input_ub, bound_method)
                     lb_tile_np = lb_tile.squeeze(0).detach().cpu().numpy() # [TH, TW, 3]
                     ub_tile_np = ub_tile.squeeze(0).detach().cpu().numpy()
                     img_lb[hl:hu, wl:wu, :] = lb_tile_np
                     img_ub[hl:hu, wl:wu, :] = ub_tile_np
+                    img_lA[hl:hu, wl:wu, :, :] = lA_tile.squeeze(0)
+                    img_uA[hl:hu, wl:wu, :, :] = uA_tile.squeeze(0)
+                    img_lbias[hl:hu, wl:wu, :] = lbias_tile.squeeze(0)
+                    img_ubias[hl:hu, wl:wu, :] = ubias_tile.squeeze(0)
 
                 if debug:
                     pbar3.update(1)
@@ -210,10 +219,14 @@ def main(setup_dict):
                 save_abstract_record(
                     save_dir=save_folder_full,
                     index = absimg_num,
-                    lower_input = input_lb_org,
-                    upper_input = input_ub_org,
+                    lower_input = input_lb.squeeze(0),
+                    upper_input = input_ub.squeeze(0),
                     lower_img=img_lb_f,
                     upper_img=img_ub_f,
+                    img_lA=img_lA,
+                    img_uA=img_uA,
+                    img_lbias=img_lbias,
+                    img_ubias=img_ubias,
                     point = base_trans,
                     direction = direction,
                     radius = radius,

scripts/render_models.py

@@ -204,7 +204,7 @@ def render_alpha(self, pose, scene_dict, eps_max=1.0):
 
         covs_pix_det = (Ms_pix_00*Ms_pix_11-Ms_pix_01*Ms_pix_10)**2+(Ms_pix_00*Ms_pix_12-Ms_pix_02*Ms_pix_10)**2+(Ms_pix_01*Ms_pix_12-Ms_pix_02*Ms_pix_11)**2
         # print(torch.min(covs_pix_det), torch.max(covs_pix_det)) 
-        covs_pix_det += depth*1e-20# May cause error
+        covs_pix_det += depth*1e-15# May cause error
 
         covs_pix_00 = Ms_pix_00**2+Ms_pix_01**2+Ms_pix_02**2
         covs_pix_01 = Ms_pix_00*Ms_pix_10+Ms_pix_01*Ms_pix_11+Ms_pix_02*Ms_pix_12
@@ -508,7 +508,7 @@ def render_alpha(self, pose, scene_dict, eps_max=1.0):
         Ms_pix_12 = Ms_pix[:, :, 1, 2]
 
         covs_pix_det = (Ms_pix_00*Ms_pix_11-Ms_pix_01*Ms_pix_10)**2+(Ms_pix_00*Ms_pix_12-Ms_pix_02*Ms_pix_10)**2+(Ms_pix_01*Ms_pix_12-Ms_pix_02*Ms_pix_11)**2
-        covs_pix_det += depth*1e-15 # May cause error
+        covs_pix_det += depth*1e-15
 
         covs_pix_00 = Ms_pix_00**2+Ms_pix_01**2+Ms_pix_02**2
         covs_pix_01 = Ms_pix_00*Ms_pix_10+Ms_pix_01*Ms_pix_11+Ms_pix_02*Ms_pix_12