code release

mesh2ply_8w.py

@@ -23,30 +23,6 @@ def obj_to_ply(obj_filename, ply_filename):
     o3d.io.write_point_cloud(ply_filename, pcd)
     print(f"Point cloud saved to {ply_filename}")
 
-# Replace 'path_to_obj.obj' with the path to your .obj file and
-# 'output_path.ply' with the desired output path for the .ply file.
-# obj_filename = './outputs/magic123-coarse-sd/new.png-a_panda_dancing@20231022-112824/save/it10000-export/model.obj'
-# ply_filename = 'a.ply'
 if __name__ == '__main__':
     # obj_to_ply(obj_filename, ply_filename)
     fire.Fire(obj_to_ply)
-# def load_obj(filename):
-#     """
-#     Load the OBJ file and return vertices and faces.
-#     """
-#     vertices = []
-#     faces = []
-
-#     with open(filename, 'r') as file:
-#         for line in file:
-#             if line.startswith('v '):
-#                 vertices.append(list(map(float, line.strip().split()[1:4])))
-#             elif line.startswith('f'):
-#                 face = [int(i.split('/')[0]) for i in line.strip().split()[1:]]
-#                 faces.append(face)
-
-#     return np.array(vertices), np.array(faces)
-
-# res = load_obj(obj_filename)
-# print(res[0].shape)
-# print(res[1].shape)

render_comp_video.py

@@ -89,13 +89,6 @@ def query_trajectory(generate_coordinates, t0, fps, frame_num):
     translation_list = [generate_coordinates(t0 + i * fps) for i in range(frame_num)]
     return translation_list
 
-# def query_trajectory(t0, fps, frame_num):
-#     R = 0.5
-#     rot_speed = 1 / 3
-#     get_location = lambda t: np.array((R * np.sin(2 * np.pi * t * rot_speed), 0, R * np.cos(2 * np.pi * t * rot_speed)))
-#     translation_list = [get_location(t0 + i * fps) for i in range(frame_num)]
-#     return translation_list
-
 to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
 def render_set_fixcam(model_path, name, iteration, views, gaussians, pipeline, background,multiview_video, fname='video_rgb.mp4', func=None, scales=None, pre_scale=False, cam_idx=25):
     render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
@@ -107,11 +100,7 @@ def render_set_fixcam(model_path, name, iteration, views, gaussians, pipeline, b
     gt_list = []
     render_list = []
     print(len(views))
-
-    # for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
-    # for idx in tqdm(range (100)):
-    # fnum = 100
-    # fnum = 12
+
     ####
     fnum = 48
     for idx in tqdm(range (fnum)):

scene/comp_scene.py

@@ -77,7 +77,7 @@ def __init__(self, args : ModelParams, gaussians : GaussianModel, load_iteration
             # 4 is not used
             # self.gaussians.load_3studio_ply(args.cloud_path, spatial_lr_scale=1, time_line=self.maxtime, step=1, position_scale=1/2.5, load_color=True) ## 4dfy
             for idx, _ in enumerate(self.gaussians):
-                _.load_3studio_ply(args.cloud_path[idx], spatial_lr_scale=1, time_line=self.maxtime, pts_num=int(2.5e4), position_scale=1/2.5, load_color=True) ## 4dfy
+                _.load_3studio_ply(args.cloud_path[idx], spatial_lr_scale=1, time_line=self.maxtime, pts_num=int(2e4), position_scale=1/2.5, load_color=True) ## 4dfy
             # self.gaussians.load_3studio_ply(cloud_path, spatial_lr_scale=1, time_line=self.maxtime, step=1, position_scale=1/2.5, load_color=True) ## 4dfy
             # self.gaussians.load_3studio_ply(cloud_path, spatial_lr_scale=1, time_line=self.maxtime, step=1, position_scale=1, load_color=True) ## imagedream
 

train_comp.py

@@ -3,15 +3,13 @@
 import os
 import torch
 
-from random import randint
 from utils.loss_utils import l1_loss, ssim, l2_loss, lpips_loss
 from gaussian_renderer.comp_renderer import render as render_comp
 from gaussian_renderer import render as render_single
 import sys
 from scene.comp_scene import Scene
 from scene.gaussian_model_nogrid import GaussianModel_nogrid as GaussianModel
 from utils.general_utils import safe_state
-import uuid
 from tqdm import tqdm
 from utils.image_utils import psnr
 from argparse import ArgumentParser, Namespace
@@ -33,10 +31,6 @@
 except ImportError:
     TENSORBOARD_FOUND = False
 
-# from guidance.zero123_utils import Zero123
-# from guidance.zeroscope_utils_hifa import ZeroScope
-# from guidance.zeroscope_utils import ZeroScope
-# from guidance.mvdream_utils import MVDream
 from guidance.sd_utils import StableDiffusion
 
 from PIL import Image
@@ -103,20 +97,6 @@ def get_rotation(prev_pos, next_pos):
     # canonical = np.array([0, 0, 1])
     return find_rotation_matrix(canonical, new_vec)
 
-# Constants
-g = 9.81  # acceleration due to gravity, m/s^2
-
-# Initial horizontal velocity calculation
-vx = 2  # m/s, to cover 4 meters in 2 seconds
-
-# To calculate the initial vertical velocity, we use the equation of motion at the peak (1 second into the jump)
-# At the peak, vertical velocity (v) = 0, acceleration (a) = -g, time (t) = 1 sec
-# We rearrange the equation v = u + at to find u: u = v - at
-vy_initial = 0 - (-g) * 1  # Initial vertical velocity
-
-    # return np.array((x, z, y))
-    # return np.array((x, y, z))
-
 def query_trajectory(generate_coordinates, t0, fps, frame_num):
     # get_location = lambda t: np.array((R * np.sin(2 * np.pi * t * rot_speed), 0, R * np.cos(2 * np.pi * t * rot_speed)))
     translation_list = [generate_coordinates(t0 + i * fps) for i in range(frame_num)]
@@ -205,13 +185,13 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
                 frame_num = viewpoint_stack.pose0_num
 
                 loader = iter(viewpoint_stack_loader)
-            if True:
-                try:
-                    data = next(loader)
-                except StopIteration:
-                    print("reset dataloader")
-                    batch_size = 1
-                    loader = iter(viewpoint_stack_loader)
+
+            try:
+                data = next(loader)
+            except StopIteration:
+                print("reset dataloader")
+                batch_size = 1
+                loader = iter(viewpoint_stack_loader)
             if (iteration - 1) == debug_from:
                 pipe.debug = True
             images = []
@@ -251,7 +231,7 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
             radii = torch.cat(radii_list,0).max(dim=0).values
             visibility_filter = torch.cat(visibility_filter_list).any(dim=0)
             image_tensor = torch.cat(images,0)
-            # print('output', image_tensor.shape) # B, C, H, W
+
             if len(out_pts):
                 out_pts = torch.stack(out_pts, 0)
 

traj_funcs/butterfly_flower.py

@@ -1,6 +1,6 @@
 import numpy as np
 
 def generate_coordinates(timestep): # [0, 1]
-    x = -2 + timestep*1.7
-    y = 0.6 - timestep*0.4
+    x = -2 + timestep * 1.5
+    y = 0.5 - timestep * 0.5
     return np.array([x, y, 0]) # x y z

traj_funcs/fish_rock.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-def generate_coordinates(t): # [0, 1]
-    z = 0.8 * np.sin(t*np.pi/2)
+def generate_coordinates(t):
+    z = 1.0 * np.sin(t*np.pi/2)
     x = -1.5 * np.cos(t*np.pi/2)
-    return np.array([x, 0, z]) # x y z
+    return np.array([x, 0, z])

utils/camera_utils.py

@@ -18,15 +18,6 @@
 
 def loadCam(args, id, cam_info, resolution_scale):
 
-
-    # resized_image_rgb = PILtoTorch(cam_info.image, resolution)
-
-    # gt_image = resized_image_rgb[:3, ...]
-    # loaded_mask = None
-
-    # if resized_image_rgb.shape[1] == 4:
-        # loaded_mask = resized_image_rgb[3:4, ...]
-
     return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, 
                   FoVx=cam_info.FovX, FoVy=cam_info.FovY, 
                   image=cam_info.image, gt_alpha_mask=None,