Fix apple#5 and apple#6.

Also fix bibtex.

Author: theNded

README.md

@@ -78,7 +78,7 @@ If you find our work useful, please cite the following paper:
 ```bibtex
 @inproceedings{Sharp2025:arxiv,
   title      = {Sharp Monocular View Synthesis in Less Than a Second},
-  author     = {Lars Mescheder and Wei Dong and Shiwei Li and Xuyang Bai and Marcel Santos and Peiyun Hu and Bruno Lecouat and Mingmin Zhen and Ama\"{e}l Delaunoyand Tian Fang and Yanghai Tsin and Stephan R. Richter and Vladlen Koltun},
+  author     = {Lars Mescheder and Wei Dong and Shiwei Li and Xuyang Bai and Marcel Santos and Peiyun Hu and Bruno Lecouat and Mingmin Zhen and Ama\"{e}l Delaunoy and Tian Fang and Yanghai Tsin and Stephan R. Richter and Vladlen Koltun},
   journal    = {arXiv preprint arXiv:2512.10685},
   year       = {2025},
   url        = {https://arxiv.org/abs/2512.10685},

src/sharp/cli/render.py

@@ -87,8 +87,8 @@ def render_gaussians(
 
     intrinsics = torch.tensor(
         [
-            [f_px, 0, (width - 1) / 2., 0],
-            [0, f_px, (height - 1) / 2., 0],
+            [f_px, 0, (width - 1) / 2.0, 0],
+            [0, f_px, (height - 1) / 2.0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ],

src/sharp/utils/gaussians.py

@@ -320,14 +320,17 @@ def load_ply(path: Path) -> tuple[Gaussians3D, SceneMetaData]:
     # Parse color space.
     color_space_index = supplement_data.get("color_space", 1)
     color_space = cs_utils.decode_color_space(color_space_index)
+    colors = torch.from_numpy(colors).view(1, -1, 3).float()
+
     if color_space == "sRGB":
-        colors = cs_utils.sRGB2linearRGB(colors)
+        # Convert to linearRGB for proper alpha blending.
+        colors = cs_utils.sRGB2linearRGB(colors.flatten(0, 1)).view(1, -1, 3)
+        color_space = "linearRGB"
 
     mean_vectors = torch.from_numpy(mean_vectors).view(1, -1, 3).float()
     quaternions = torch.from_numpy(quaternions).view(1, -1, 4).float()
     singular_values = torch.exp(torch.from_numpy(scale_logits).view(1, -1, 3)).float()
     opacities = torch.sigmoid(torch.from_numpy(opacity_logits).view(1, -1)).float()
-    colors = torch.from_numpy(colors).view(1, -1, 3).float()
 
     gaussians = Gaussians3D(
         mean_vectors=mean_vectors,