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from utils import quat2rot
import numpy as np
class Gaussian:
def __init__(self, positions, scales, rotations, features_dc, features_rest, opacity):
self.num_gaussians = positions.shape[0]
self.positions = positions
self.scales = scales
self.rotations = rotations
# self.scales = np.exp(scales)
# rot_length = np.repeat(np.linalg.norm(rotations, axis=1), 4).reshape(-1, 4)
# self.rotations = rotations / rot_length
self.features_dc = features_dc
self.features_rest = features_rest
self.opacity = opacity
def empty_with_cap(cap: int) -> "Gaussian":
positions = np.zeros((cap, 3))
scales = np.zeros((cap, 3))
rotations = np.zeros((cap, 4))
features_dc = np.zeros((cap, 1, 3))
features_rest = np.zeros((cap, 15, 3))
opacity = np.zeros((cap, 1))
gaussian = Gaussian(positions, scales, rotations, features_dc, features_rest, opacity)
gaussian.num_gaussians = 0
return gaussian
@property
def sh(self):
return np.concatenate((self.features_dc, self.features_rest), axis=1)
def calc(self, x, id: int, scale: float):
x = x * scale
S = np.diag(self.scales[id])
R = quat2rot(self.rotations[id])
M = S @ R
Sigma = M.T @ M
Sigma = np.linalg.inv(Sigma)
return np.dot(x, Sigma @ x)
# M = R @ S
# return np.dot(x, M @ M.T @ x)
def clip_to_box(self, min: np.ndarray, max: np.ndarray):
indices = ((self.positions > min) & (self.positions < max)).all(axis=1)
self.apply_filter(indices)
def apply_filter(self, filter):
self.positions = self.positions[filter]
self.scales = self.scales[filter]
self.rotations = self.rotations[filter]
self.features_dc = self.features_dc[filter]
self.features_rest = self.features_rest[filter]
self.opacity = self.opacity[filter]
self.num_gaussians = self.positions.shape[0]
def add(self, position, scale, rotation, features_dc, features_rest, opacity):
n = self.num_gaussians
self.positions[n] = position
self.scales[n] = scale
self.rotations[n] = rotation
self.features_dc[n] = features_dc
self.features_rest[n] = features_rest
self.opacity[n] = opacity
self.num_gaussians += 1
return n
def concat(self, other: "Gaussian"):
self.positions = np.concatenate((self.positions, other.positions), axis=0)
self.scales = np.concatenate((self.scales, other.scales), axis=0)
self.rotations = np.concatenate((self.rotations, other.rotations), axis=0)
self.features_dc = np.concatenate((self.features_dc, other.features_dc), axis=0)
self.features_rest = np.concatenate((self.features_rest, other.features_rest), axis=0)
self.opacity = np.concatenate((self.opacity, other.opacity), axis=0)
self.num_gaussians = self.num_gaussians + other.num_gaussians
def replace(self, id1: int, id2: int,
position, scale, rotation, sh, opacity):
# 用新的 gaussian 替换 id1 位置的 gaussian
self.positions[id1] = position
self.scales[id1] = scale
self.rotations[id1] = rotation
self.features_dc[id1] = sh[0, :]
self.features_rest[id1] = sh[1:, :]
self.opacity[id1] = opacity
# 把最后一个 gaussian 放到 id2 的位置上
n = self.num_gaussians
self.positions[id2] = self.positions[n - 1]
self.scales[id2] = self.scales[n - 1]
self.rotations[id2] = self.rotations[n - 1]
self.features_dc[id2] = self.features_dc[n - 1]
self.features_rest[id2] = self.features_rest[n - 1]
self.opacity[id2] = self.opacity[n - 1]
self.num_gaussians -= 1
pass
def copy(self) -> "Gaussian":
positions = np.copy(self.positions)
scales = np.copy(self.scales)
rotations = np.copy(self.rotations)
features_dc = np.copy(self.features_dc)
features_rest = np.copy(self.features_rest)
opacity = np.copy(self.opacity)
return Gaussian(positions, scales, rotations, features_dc, features_rest, opacity)
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