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눈알 빠질거 같다.. #1276

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68 changes: 68 additions & 0 deletions gaussian_renderer/__init__.py
View file Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -126,3 +126,71 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
}

return out




##################### 추가함수 #####################
def render_reflected_gaussians(viewpoint_camera, pc: GaussianModel, pipe, bg_color: torch.Tensor, mirror_transform: torch.Tensor, gaussians_to_reflect_mask: torch.Tensor, scaling_modifier=1.0):
reflected_attrs = pc.reflect(mirror_transform, gaussians_to_reflect_mask)

if reflected_attrs is None or reflected_attrs["xyz"].shape[0] == 0:
image_height = int(viewpoint_camera.image_height)
image_width = int(viewpoint_camera.image_width)
return {"render": torch.full((3, image_height, image_width), bg_color[0].item(), device="cuda"), "radii": torch.zeros(0, device="cuda")}

tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)

# 카메라 시선벡터 반사
orig_campos = viewpoint_camera.camera_center
campos_hom = torch.cat([orig_campos, torch.ones(1, device="cuda")], dim=0)
reflected_campos_hom = campos_hom @ mirror_transform.T
reflected_campos = reflected_campos_hom[:3]

raster_settings = GaussianRasterizationSettings(
image_height=int(viewpoint_camera.image_height),
image_width=int(viewpoint_camera.image_width),
tanfovx=tanfovx,
tanfovy=tanfovy,
bg=bg_color,
scale_modifier=scaling_modifier,
viewmatrix=viewpoint_camera.world_view_transform,
projmatrix=viewpoint_camera.full_proj_transform,
sh_degree=pc.active_sh_degree,
campos=reflected_campos,
prefiltered=False,
debug=pipe.debug,
antialiasing=pipe.antialiasing
)

rasterizer = GaussianRasterizer(raster_settings=raster_settings)

means3D = reflected_attrs["xyz"]
rotations = torch.nn.functional.normalize(reflected_attrs["rotation"])
scales = torch.exp(reflected_attrs["scaling"])
opacity = torch.sigmoid(reflected_attrs["opacity"])
shs = torch.cat((reflected_attrs["features_dc"], reflected_attrs["features_rest"]), dim=1)

screenspace_points = torch.zeros_like(means3D, dtype=means3D.dtype, requires_grad=True, device="cuda") + 0
try:
screenspace_points.retain_grad()
except:
pass

# Rasterize
rendered_image, radii, _ = rasterizer(
means3D = means3D,
means2D = screenspace_points,
shs = shs,
colors_precomp = None,
opacities = opacity,
scales = scales,
rotations = rotations,
cov3D_precomp = None
)

return {
"render": rendered_image.clamp(0, 1),
"radii": radii
}
57 changes: 57 additions & 0 deletions propose_mirror_plane.py
View file Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,57 @@
import numpy as np
import os
import json
from argparse import ArgumentParser
import sys

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))

from scene.colmap_loader import read_points3D_binary

def propose_plane_robust(points_bin_path: str, output_json_path: str):
print(f"Analyzing point cloud from: {points_bin_path}")
try:
xyz, _, _ = read_points3D_binary(points_bin_path)
if xyz.shape[0] == 0:
raise ValueError("Point cloud is empty.")

y_coords = xyz[:, 1]

# 모든 가우시안의 y값의 하위 5%를 실질적 바닥으로 둠
y_percentile_5 = np.percentile(y_coords, 5)
y_mean = np.mean(y_coords)

print(f"Point cloud Y-axis 5th percentile: {y_percentile_5:.3f}")
print(f"Point cloud Y-axis mean: {y_mean:.3f}")

# 바닥에서 y값 평균 ~ 바닥 거리 만큼 뺌 -> 바닥 아래에 반사 평면 위치시킴
plane_y_value = y_percentile_5 - (y_mean - y_percentile_5)

# 법선벡터는 (0, 1, 0)으로 고정
plane_params = {
"a": 0.0,
"b": 1.0,
"c": 0.0,
"d": -plane_y_value
}

os.makedirs(os.path.dirname(output_json_path), exist_ok=True)
with open(output_json_path, 'w') as f:
json.dump(plane_params, f, indent=4)

print(f"\nSuccessfully proposed robust mirror plane: y = {plane_y_value:.3f}")
print(f"Plane parameters saved to: {output_json_path}")

except Exception as e:
print(f"Error: Failed to propose mirror plane. {e}")

if __name__ == "__main__":
parser = ArgumentParser(description="Propose a robust mirror plane from a COLMAP sparse point cloud.")
parser.add_argument("-s", "--source_path", required=True, type=str, help="Path to the COLMAP dataset directory")
parser.add_argument("-m", "--model_path", required=True, type=str, help="Path to the output model directory where the plane file will be saved")
args = parser.parse_args()

points_3d_bin = os.path.join(args.source_path, "sparse/0/points3D.bin")
output_json = os.path.join(args.model_path, "mirror_plane.json")

propose_plane_robust(points_3d_bin, output_json)
67 changes: 57 additions & 10 deletions scene/gaussian_model.py
View file Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,8 @@
from plyfile import PlyData, PlyElement
from utils.sh_utils import RGB2SH
from simple_knn._C import distCUDA2
from utils.graphics_utils import BasicPointCloud
from utils.general_utils import strip_symmetric, build_scaling_rotation
from utils.graphics_utils import BasicPointCloud, geom_transform_points
from utils.general_utils import strip_symmetric, build_scaling_rotation, quat_from_matrix, build_rotation

try:
from diff_gaussian_rasterization import SparseGaussianAdam
Expand Down Expand Up @@ -318,14 +318,18 @@ def replace_tensor_to_optimizer(self, tensor, name):
for group in self.optimizer.param_groups:
if group["name"] == name:
stored_state = self.optimizer.state.get(group['params'][0], None)
stored_state["exp_avg"] = torch.zeros_like(tensor)
stored_state["exp_avg_sq"] = torch.zeros_like(tensor)

del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
self.optimizer.state[group['params'][0]] = stored_state

optimizable_tensors[group["name"]] = group["params"][0]
if stored_state is not None:
stored_state["exp_avg"] = torch.zeros_like(tensor)
stored_state["exp_avg_sq"] = torch.zeros_like(tensor)

del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
self.optimizer.state[group['params'][0]] = stored_state

optimizable_tensors[group["name"]] = group["params"][0]
else:
group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors

def _prune_optimizer(self, mask):
Expand Down Expand Up @@ -471,3 +475,46 @@ def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, radi
def add_densification_stats(self, viewspace_point_tensor, update_filter):
self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
self.denom[update_filter] += 1




##################### 추가함수 #####################
@torch.no_grad()
def reflect(self, mirror_transform, reflect_mask):
from utils.graphics_utils import geom_transform_points
from utils.general_utils import build_rotation

if reflect_mask.sum() == 0:
return None

object_rotation_q = self.get_rotation[reflect_mask]
object_xyz = self.get_xyz[reflect_mask]

# 센터 반사
reflected_xyz = geom_transform_points(object_xyz, mirror_transform.T)

# 회전 반사
H_3x3 = mirror_transform[:3, :3]
R = build_rotation(object_rotation_q)
R_reflected = H_3x3 @ R
# 재직교화(SVD) + det(+1) 강제
U, S, Vt = torch.linalg.svd(R_reflected) # (N,3,3)
R_reflected = U @ Vt
det = torch.det(R_reflected)
neg = det < 0
if neg.any():
Vt[neg, -1, :] *= -1.0
R_reflected = U @ Vt

reflected_rotation_q = quat_from_matrix(R_reflected)

# 반사시킨 속성 반환
return {
"xyz": reflected_xyz,
"rotation": torch.nn.functional.normalize(reflected_rotation_q, p=2, dim=1),
"scaling": self._scaling[reflect_mask],
"opacity": self._opacity[reflect_mask],
"features_dc": self._features_dc[reflect_mask],
"features_rest": self._features_rest[reflect_mask]
}
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