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on
Zero
Running
on
Zero
| import torch | |
| from .gaussian_utils import render, GaussianModel | |
| class GaussianRenderer: | |
| def __init__(self, renderer_config=None): | |
| if 'scaling_activation_type' not in renderer_config: | |
| renderer_config['scaling_activation_type'] = 'exp' | |
| if 'scale_min_act' not in renderer_config: | |
| renderer_config['scale_min_act'] = 1 | |
| renderer_config['scale_max_act'] = 1 | |
| renderer_config['scale_multi_act'] = 0.1 | |
| self.gaussian_model = GaussianModel(sh_degree=renderer_config.sh_degree, | |
| scaling_activation_type=renderer_config.scaling_activation_type, | |
| scale_min_act=renderer_config.scale_min_act, | |
| scale_max_act=renderer_config.scale_max_act, | |
| scale_multi_act=renderer_config.scale_multi_act) | |
| self.img_height = renderer_config.img_height | |
| self.img_width = renderer_config.img_width | |
| self.bg_color = renderer_config.bg_color if 'bg_color' in renderer_config else (1.0, 1.0, 1.0) | |
| def render(self, latent, output_fxfycxcy, output_c2ws, rescale=None, render_size=None): | |
| if render_size is None: | |
| img_height, img_width = self.img_height, self.img_width | |
| else: | |
| img_height, img_width = render_size | |
| if rescale is None: | |
| rescale = torch.ones(latent.shape[0]).to(latent) | |
| shs_dim = (self.gaussian_model.sh_degree + 1) ** 2 * 3 | |
| xyz, features, opacity, scaling, rotation = latent.split([3, shs_dim, 1, 3, 4], dim=-1) | |
| features = features.reshape(features.shape[0], -1, shs_dim//3, 3) | |
| bs, vs = output_fxfycxcy.shape[:2] | |
| images = torch.zeros(bs, vs, 3, img_height, img_width, dtype=torch.float32, device=output_c2ws.device) | |
| alphas = torch.zeros(bs, vs, 1, img_height, img_width, dtype=torch.float32, device=output_c2ws.device) | |
| depths = torch.zeros(bs, vs, 1, img_height, img_width, dtype=torch.float32, device=output_c2ws.device) | |
| for idx in range(bs): | |
| pc = self.gaussian_model.set_data(xyz[idx], features[idx], scaling[idx], rotation[idx], opacity[idx], rescale[idx]) | |
| for vidx in range(vs): | |
| render_results = render(pc, img_height, img_width, output_c2ws[idx, vidx], output_fxfycxcy[idx, vidx], self.bg_color) | |
| image = render_results['render'] | |
| alpha = render_results['alpha'] | |
| depth = render_results['depth'] | |
| images[idx, vidx] = image | |
| alphas[idx, vidx] = alpha | |
| depths[idx, vidx] = depth | |
| results = {'image': images, 'alpha': alphas, 'depth': depths} | |
| return results | |