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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Optional
import numpy as np
import torch
@dataclass
class Gaussians:
"""3DGS parameters, all in world space"""
means: torch.Tensor # world points, "batch gaussian dim"
scales: torch.Tensor # scales_std, "batch gaussian 3"
rotations: torch.Tensor # world_quat_wxyz, "batch gaussian 4"
harmonics: torch.Tensor # world SH, "batch gaussian 3 d_sh"
opacities: torch.Tensor # opacity | opacity SH, "batch gaussian" | "batch gaussian 1 d_sh"
@dataclass
class Prediction:
depth: np.ndarray # N, H, W
is_metric: int
sky: np.ndarray | None = None # N, H, W
conf: np.ndarray | None = None # N, H, W
extrinsics: np.ndarray | None = None # N, 4, 4
intrinsics: np.ndarray | None = None # N, 3, 3
processed_images: np.ndarray | None = None # N, H, W, 3 - processed images for visualization
gaussians: Gaussians | None = None # 3D gaussians
aux: dict[str, Any] = None #
scale_factor: Optional[float] = None # metric scale