| | import torch |
| | import numpy as np |
| | from utils.graphics_utils import get_full_proj_matrix |
| | from tqdm import tqdm |
| | from copy import deepcopy |
| | def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor: |
| | """ |
| | Normalize vector lengths. |
| | """ |
| | return vectors / (torch.norm(vectors, dim=-1, keepdim=True)) |
| |
|
| | def create_cam2world_matrix(forward_vector, origin): |
| | """ |
| | Takes in the direction the camera is pointing and the camera origin and returns a cam2world matrix. |
| | Works on batches of forward_vectors, origins. Assumes y-axis is up and that there is no camera roll. |
| | """ |
| |
|
| | forward_vector = normalize_vecs(forward_vector) |
| | up_vector = torch.tensor([0, 1, 0], dtype=torch.float, device=origin.device).expand_as(forward_vector) |
| |
|
| | right_vector = -normalize_vecs(torch.cross(up_vector, forward_vector, dim=-1)) |
| | up_vector = normalize_vecs(torch.cross(forward_vector, right_vector, dim=-1)) |
| |
|
| | rotation_matrix = torch.eye(4, device=origin.device).unsqueeze(0).repeat(forward_vector.shape[0], 1, 1) |
| | rotation_matrix[:, :3, :3] = torch.stack((right_vector, up_vector, forward_vector), axis=-1) |
| |
|
| | translation_matrix = torch.eye(4, device=origin.device).unsqueeze(0).repeat(forward_vector.shape[0], 1, 1) |
| | translation_matrix[:, :3, 3] = origin |
| | cam2world = (translation_matrix @ rotation_matrix)[:, :, :] |
| | assert(cam2world.shape[1:] == (4, 4)) |
| | return cam2world |
| |
|
| |
|
| | class LookAtPoseSampler: |
| | """ |
| | Same as GaussianCameraPoseSampler, except the |
| | camera is specified as looking at 'lookat_position', a 3-vector. |
| | |
| | Example: |
| | For a camera pose looking at the origin with the camera at position [0, 0, 1]: |
| | cam2world = LookAtPoseSampler.sample(math.pi/2, math.pi/2, torch.tensor([0, 0, 0]), radius=1) |
| | """ |
| | @staticmethod |
| | def sample(horizontal_mean, vertical_mean, lookat_position,FoVx,FoVy, horizontal_stddev=0, vertical_stddev=0, radius=1, batch_size=1, device='cuda:0'): |
| | h = torch.randn((batch_size, 1), device=device) * horizontal_stddev + horizontal_mean |
| | v = torch.randn((batch_size, 1), device=device) * vertical_stddev + vertical_mean |
| | v = torch.clamp(v, 1e-5, np.pi - 1e-5) |
| |
|
| | theta = h |
| | v = v / np.pi |
| | phi = torch.arccos(1 - 2*v) |
| |
|
| | camera_origins = torch.zeros((batch_size, 3), device=device) |
| |
|
| | camera_origins[:, 0:1] = radius*torch.sin(phi) * torch.cos(np.pi-theta) |
| | camera_origins[:, 2:3] = radius*torch.sin(phi) * torch.sin(np.pi-theta) |
| | camera_origins[:, 1:2] = radius*torch.cos(phi) |
| |
|
| | |
| | forward_vectors = normalize_vecs(lookat_position - camera_origins) |
| | c2w=create_cam2world_matrix(forward_vectors, camera_origins) |
| | |
| | w2c=torch.linalg.inv(c2w).squeeze(0)@torch.tensor([[1 ,0 ,0 ,0 ], |
| | [0 ,-1,0 ,0 ], |
| | [0 ,0 ,-1,0 ], |
| | [0 ,0 ,0 ,1 ]],dtype=torch.float32,device=device) |
| | R = torch.transpose(w2c[:3,:3],0,1).cpu().numpy() |
| | T= w2c[:3, 3].cpu().numpy() |
| | c2w=torch.linalg.inv(w2c) |
| | |
| |
|
| | return w2c,c2w |
| | |
| |
|
| | def generate_novel_view_poses(tracking_info,image_size=512,tanfov=1/24.0,pitch_range = 0.3,yaw_range = 0.35,num_keyframes=120): |
| | |
| | |
| | camera_center=tracking_info['c2w_cam'][0,:3,3] |
| | device=tracking_info['c2w_cam'].device |
| | circle_cam_params=[] |
| | result_cam_params=[] |
| | FoVx=tanfov |
| | FoVy=tanfov |
| | radius=camera_center.square().sum().sqrt() |
| | |
| | lookat_position=[0.0,0.75,0.0] |
| | print("Generate multi-view poses for rendering") |
| | |
| | for frame_idx in tqdm(range(num_keyframes)): |
| | w2c_cam,c2w_cam=LookAtPoseSampler.sample( |
| | horizontal_mean=3.14 / 2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes)), |
| | vertical_mean=3.14 / 2 - 0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes)), |
| | lookat_position=torch.Tensor(lookat_position).to(device),FoVx=FoVx,FoVy=FoVy ,radius=radius, device=device) |
| | view_matrix,full_proj_matrix=get_full_proj_matrix(w2c_cam,tanfov) |
| | circle_cam_params.append({ |
| | "world_view_transform":view_matrix.unsqueeze(0),"full_proj_transform":full_proj_matrix.unsqueeze(0), |
| | 'tanfovx':torch.tensor([FoVx],device=device),'tanfovy':torch.tensor([FoVy],device=device), |
| | 'image_height':torch.tensor([image_size],device=device),'image_width':torch.tensor([image_size],device=device), |
| | 'camera_center':c2w_cam[:3,3].unsqueeze(0) |
| | }) |
| | |
| | |
| | |
| |
|
| | return circle_cam_params |
