image fitting cleanup

examples/image_fitting.py

@@ -2,7 +2,7 @@
 import os
 import time
 from pathlib import Path
-from typing import Optional, Literal
+from typing import Literal, Optional
 
 import numpy as np
 import torch
@@ -104,7 +104,7 @@ def train(
         for iter in range(iterations):
             start = time.time()
 
-            renders, _ = rasterize_fnc(
+            renders = rasterize_fnc(
                 self.means,
                 self.quats / self.quats.norm(dim=-1, keepdim=True),
                 self.scales,
@@ -115,8 +115,8 @@ def train(
                 self.W,
                 self.H,
                 packed=False,
-            )
-            out_img = renders[0].squeeze(0)
+            )[0]
+            out_img = renders[0]
             torch.cuda.synchronize()
             times[0] += time.time() - start
             loss = mse_loss(out_img, self.gt_image)
@@ -130,8 +130,6 @@ def train(
 
             if save_imgs and iter % 5 == 0:
                 frames.append((out_img.detach().cpu().numpy() * 255).astype(np.uint8))
-            # break
-
         if save_imgs:
             # save them as a gif with PIL
             frames = [Image.fromarray(frame) for frame in frames]

gsplat/rendering.py

@@ -1082,7 +1082,7 @@ def rasterization_2dgs(
 
         **render_normals**: The rendered normals. [C, height, width, 3].
 
-        **render_normals_from_depth: surface normal from depth. [C, height, width, 3]
+        **render_normals_from_depth**: surface normal from depth. [C, height, width, 3]
 
         **render_distort**: The rendered distortions. [C, height, width, 1].
         L1 version, different from L2 version in 2DGS paper.
@@ -1278,6 +1278,8 @@ def rasterization_2dgs(
 
     render_normals = render_normals @ torch.linalg.inv(viewmats)[0, :3, :3].T
 
+    # Note(ruilong): To align with the rasterization function, this should return
+    # (render_colors, ..., meta) instead of (render_colors, render_alphas), meta.
     return (
         render_colors,
         render_alphas,