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| ### | |
| # Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr | |
| # For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. | |
| # All rights reserved. | |
| ### | |
| import numpy as np | |
| class GSParams: | |
| def __init__(self): | |
| self.sh_degree = 3 | |
| self.images = "images" | |
| self.resolution = -1 | |
| self.white_background = False | |
| self.data_device = "cuda" | |
| self.eval = False | |
| self.use_depth = False | |
| self.iterations = 2990#3_000 | |
| self.position_lr_init = 0.00016 | |
| self.position_lr_final = 0.0000016 | |
| self.position_lr_delay_mult = 0.01 | |
| self.position_lr_max_steps = 2990#3_000 | |
| self.feature_lr = 0.0025 | |
| self.opacity_lr = 0.05 | |
| self.scaling_lr = 0.005 | |
| self.rotation_lr = 0.001 | |
| self.percent_dense = 0.01 | |
| self.lambda_dssim = 0.2 | |
| self.densification_interval = 100 | |
| self.opacity_reset_interval = 3001 # To prevent from saving right after reset opacity | |
| self.densify_from_iter = 500 | |
| self.densify_until_iter = 15_000 | |
| self.densify_grad_threshold = 0.0002 | |
| self.convert_SHs_python = False | |
| self.compute_cov3D_python = False | |
| self.debug = False | |
| class CameraParams: | |
| def __init__(self, H: int = 512, W: int = 512): | |
| self.H = H | |
| self.W = W | |
| self.focal = (5.8269e+02, 5.8269e+02) | |
| self.fov = (2*np.arctan(self.W / (2*self.focal[0])), 2*np.arctan(self.H / (2*self.focal[1]))) | |
| self.K = np.array([ | |
| [self.focal[0], 0., self.W/2], | |
| [0., self.focal[1], self.H/2], | |
| [0., 0., 1.], | |
| ]).astype(np.float32) |