Merge branch 'release' into develop

README.md

@@ -71,8 +71,8 @@ The optimizer uses PyTorch and CUDA extensions in a Python environment to produc
 
 ### Software Requirements
 - Conda (recommended for easy setup)
-- C++ Compiler for PyTorch extensions (we *recommend* Visual Studio 2019 for Windows)
-- CUDA 11 SDK for PyTorch extensions (we used 11.8)
+- C++ Compiler for PyTorch extensions (we used Visual Studio 2019 for Windows)
+- CUDA SDK 11.7+ for PyTorch extensions (we used 11.8, known issues with 11.6)
 - C++ Compiler and CUDA SDK must be compatible
 
 ### Setup
@@ -269,8 +269,8 @@ We provide two interactive iewers for our method: remote and real-time. Our view
 - CUDA-ready GPU with Compute Capability 7.0+ (only for Real-Time Viewer)
 
 ### Software Requirements
-- C++ Compiler (we *recommend* Visual Studio 2019 for Windows)
-- CUDA 11 Developer SDK (we used 11.8)
+- C++ Compiler (we used Visual Studio 2019 for Windows)
+- CUDA SDK 11 (we used 11.8)
 - CMake (recent version, we used 3.24)
 - 7zip (only on Windows)
 

arguments/__init__.py

@@ -72,7 +72,7 @@ class OptimizationParams(ParamGroup):
         self.position_lr_init = 0.00016
         self.position_lr_final = 0.0000016
         self.position_lr_delay_mult = 0.01
-        self.posititon_lr_max_steps = 30_000
+        self.position_lr_max_steps = 30_000
         self.feature_lr = 0.0025
         self.opacity_lr = 0.05
         self.scaling_lr = 0.001

scene/gaussian_model.py

@@ -126,7 +126,7 @@ class GaussianModel:
         self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
                                                     lr_final=training_args.position_lr_final*self.spatial_lr_scale,
                                                     lr_delay_mult=training_args.position_lr_delay_mult,
-                                                    max_steps=training_args.posititon_lr_max_steps)
+                                                    max_steps=training_args.position_lr_max_steps)
 
     def update_learning_rate(self, iteration):
         ''' Learning rate scheduling per step '''

train.py

@@ -162,9 +162,9 @@ def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_i
                     images = torch.cat((images, image.unsqueeze(0)), dim=0)
                     gts = torch.cat((gts, gt_image.unsqueeze(0)), dim=0)
                     if tb_writer and (idx < 5):
-                        tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image, global_step=iteration)
+                        tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
                         if iteration == testing_iterations[0]:
-                            tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image, global_step=iteration)
+                            tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration)
 
                 l1_test = l1_loss(images, gts)
                 psnr_test = psnr(images, gts).mean()            
@@ -204,4 +204,4 @@ if __name__ == "__main__":
     training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations)
 
     # All done
-    print("\nTraining complete.")
+    print("\nTraining complete.")