diff --git a/examples/simple_trainer.py b/examples/simple_trainer.py
index 3e544201..52e7a233 100644
--- a/examples/simple_trainer.py
+++ b/examples/simple_trainer.py
@@ -2,6 +2,7 @@
 import math
 import os
 import time
+import struct
 from dataclasses import dataclass, field
 from collections import defaultdict
 from typing import Dict, List, Optional, Tuple, Union
@@ -84,6 +85,8 @@ class Config:
     eval_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
     # Steps to save the model
     save_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
+    # Steps to save the model as ply
+    ply_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
 
     # Initialization strategy
     init_type: str = "sfm"
@@ -164,6 +167,7 @@ class Config:
     def adjust_steps(self, factor: float):
         self.eval_steps = [int(i * factor) for i in self.eval_steps]
         self.save_steps = [int(i * factor) for i in self.save_steps]
+        self.ply_steps = [int(i * factor) for i in self.ply_steps]
         self.max_steps = int(self.max_steps * factor)
         self.sh_degree_interval = int(self.sh_degree_interval * factor)
 
@@ -181,6 +185,97 @@ def adjust_steps(self, factor: float):
             assert_never(strategy)
 
 
+def save_ply(splats: torch.nn.ParameterDict, dir: str, colors: torch.Tensor = None):
+    # Convert all tensors to numpy arrays in one go
+    print(f"Saving ply to {dir}")
+    numpy_data = {k: v.detach().cpu().numpy() for k, v in splats.items()}
+
+    means = numpy_data["means"]
+    scales = numpy_data["scales"]
+    quats = numpy_data["quats"]
+    opacities = numpy_data["opacities"]
+
+    sh0 = numpy_data["sh0"].transpose(0, 2, 1).reshape(means.shape[0], -1)
+    shN = numpy_data["shN"].transpose(0, 2, 1).reshape(means.shape[0], -1)
+
+    # Create a mask to identify rows with NaN or Inf in any of the numpy_data arrays
+    invalid_mask = (
+        np.isnan(means).any(axis=1)
+        | np.isinf(means).any(axis=1)
+        | np.isnan(scales).any(axis=1)
+        | np.isinf(scales).any(axis=1)
+        | np.isnan(quats).any(axis=1)
+        | np.isinf(quats).any(axis=1)
+        | np.isnan(opacities).any(axis=0)
+        | np.isinf(opacities).any(axis=0)
+        | np.isnan(sh0).any(axis=1)
+        | np.isinf(sh0).any(axis=1)
+        | np.isnan(shN).any(axis=1)
+        | np.isinf(shN).any(axis=1)
+    )
+
+    # Filter out rows with NaNs or Infs from all data arrays
+    means = means[~invalid_mask]
+    scales = scales[~invalid_mask]
+    quats = quats[~invalid_mask]
+    opacities = opacities[~invalid_mask]
+    sh0 = sh0[~invalid_mask]
+    shN = shN[~invalid_mask]
+
+    num_points = means.shape[0]
+
+    with open(dir, "wb") as f:
+        # Write PLY header
+        f.write(b"ply\n")
+        f.write(b"format binary_little_endian 1.0\n")
+        f.write(f"element vertex {num_points}\n".encode())
+        f.write(b"property float x\n")
+        f.write(b"property float y\n")
+        f.write(b"property float z\n")
+        f.write(b"property float nx\n")
+        f.write(b"property float ny\n")
+        f.write(b"property float nz\n")
+
+        if colors is not None:
+            for j in range(colors.shape[1]):
+                f.write(f"property float f_dc_{j}\n".encode())
+        else:
+            for i, data in enumerate([sh0, shN]):
+                prefix = "f_dc" if i == 0 else "f_rest"
+                for j in range(data.shape[1]):
+                    f.write(f"property float {prefix}_{j}\n".encode())
+
+        f.write(b"property float opacity\n")
+
+        for i in range(scales.shape[1]):
+            f.write(f"property float scale_{i}\n".encode())
+        for i in range(quats.shape[1]):
+            f.write(f"property float rot_{i}\n".encode())
+
+        f.write(b"end_header\n")
+
+        # Write vertex data
+        for i in range(num_points):
+            f.write(struct.pack("<fff", *means[i]))  # x, y, z
+            f.write(struct.pack("<fff", 0, 0, 0))  # nx, ny, nz (zeros)
+
+            if colors is not None:
+                color = colors.detach().cpu().numpy()
+                for j in range(color.shape[1]):
+                    f_dc = (color[i, j] - 0.5) / 0.2820947917738781
+                    f.write(struct.pack("<f", f_dc))
+            else:
+                for data in [sh0, shN]:
+                    for j in range(data.shape[1]):
+                        f.write(struct.pack("<f", data[i, j]))
+
+            f.write(struct.pack("<f", opacities[i]))  # opacity
+
+            for data in [scales, quats]:
+                for j in range(data.shape[1]):
+                    f.write(struct.pack("<f", data[i, j]))
+
+
 def create_splats_with_optimizers(
     parser: Parser,
     init_type: str = "sfm",
@@ -283,6 +378,8 @@ def __init__(
         os.makedirs(self.stats_dir, exist_ok=True)
         self.render_dir = f"{cfg.result_dir}/renders"
         os.makedirs(self.render_dir, exist_ok=True)
+        self.ply_dir = f"{cfg.result_dir}/ply"
+        os.makedirs(self.ply_dir, exist_ok=True)
 
         # Tensorboard
         self.writer = SummaryWriter(log_dir=f"{cfg.result_dir}/tb")
@@ -723,6 +820,20 @@ def train(self):
                 torch.save(
                     data, f"{self.ckpt_dir}/ckpt_{step}_rank{self.world_rank}.pt"
                 )
+            if step in [i - 1 for i in cfg.ply_steps] or step == max_steps - 1:
+                rgb = None
+                if self.cfg.app_opt:
+                    # eval at origin to bake the appeareance into the colors
+                    rgb = self.app_module(
+                        features=self.splats["features"],
+                        embed_ids=None,
+                        dirs=torch.zeros_like(self.splats["means"][None, :, :]),
+                        sh_degree=sh_degree_to_use,
+                    )
+                    rgb = rgb + self.splats["colors"]
+                    rgb = torch.sigmoid(rgb).squeeze(0)
+
+                save_ply(self.splats, f"{self.ply_dir}/point_cloud_{step}.ply", rgb)
 
             if isinstance(self.cfg.strategy, DefaultStrategy):
                 self.cfg.strategy.step_post_backward(