preprocessing/depth_anything_v3/utils/gsply_helpers.py

# 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 pathlib import Path
from typing import Optional
import numpy as np
import torch
from einops import rearrange, repeat
from plyfile import PlyData, PlyElement
from torch import Tensor

from ..specs import Gaussians


def construct_list_of_attributes(num_rest: int) -> list[str]:
    attributes = ["x", "y", "z", "nx", "ny", "nz"]
    for i in range(3):
        attributes.append(f"f_dc_{i}")
    for i in range(num_rest):
        attributes.append(f"f_rest_{i}")
    attributes.append("opacity")
    for i in range(3):
        attributes.append(f"scale_{i}")
    for i in range(4):
        attributes.append(f"rot_{i}")
    return attributes


def export_ply(
    means: Tensor,  # "gaussian 3"
    scales: Tensor,  # "gaussian 3"
    rotations: Tensor,  # "gaussian 4"
    harmonics: Tensor,  # "gaussian 3 d_sh"
    opacities: Tensor,  # "gaussian"
    path: Path,
    shift_and_scale: bool = False,
    save_sh_dc_only: bool = True,
    match_3dgs_mcmc_dev: Optional[bool] = False,
):
    if shift_and_scale:
        # Shift the scene so that the median Gaussian is at the origin.
        means = means - means.median(dim=0).values

        # Rescale the scene so that most Gaussians are within range [-1, 1].
        scale_factor = means.abs().quantile(0.95, dim=0).max()
        means = means / scale_factor
        scales = scales / scale_factor

    rotations = rotations.detach().cpu().numpy()

    # Since current model use SH_degree = 4,
    # which require large memory to store, we can only save the DC band to save memory.
    f_dc = harmonics[..., 0]
    f_rest = harmonics[..., 1:].flatten(start_dim=1)

    if match_3dgs_mcmc_dev:
        sh_degree = 3
        n_rest = 3 * (sh_degree + 1) ** 2 - 3
        f_rest = repeat(
            torch.zeros_like(harmonics[..., :1]), "... i -> ... (n i)", n=(n_rest // 3)
        ).flatten(start_dim=1)
        dtype_full = [
            (attribute, "f4")
            for attribute in construct_list_of_attributes(num_rest=n_rest)
            if attribute not in ("nx", "ny", "nz")
        ]
    else:
        dtype_full = [
            (attribute, "f4")
            for attribute in construct_list_of_attributes(
                0 if save_sh_dc_only else f_rest.shape[1]
            )
        ]
    elements = np.empty(means.shape[0], dtype=dtype_full)
    attributes = [
        means.detach().cpu().numpy(),
        torch.zeros_like(means).detach().cpu().numpy(),
        f_dc.detach().cpu().contiguous().numpy(),
        f_rest.detach().cpu().contiguous().numpy(),
        opacities[..., None].detach().cpu().numpy(),
        scales.log().detach().cpu().numpy(),
        rotations,
    ]
    if match_3dgs_mcmc_dev:
        attributes.pop(1)  # dummy normal is not needed
    elif save_sh_dc_only:
        attributes.pop(3)  # remove f_rest from attributes

    attributes = np.concatenate(attributes, axis=1)
    elements[:] = list(map(tuple, attributes))
    path.parent.mkdir(exist_ok=True, parents=True)
    PlyData([PlyElement.describe(elements, "vertex")]).write(path)


def inverse_sigmoid(x):
    return torch.log(x / (1 - x))


def save_gaussian_ply(
    gaussians: Gaussians,
    save_path: str,
    ctx_depth: torch.Tensor,  # depth of input views; for getting shape and filtering, "v h w 1"
    shift_and_scale: bool = False,
    save_sh_dc_only: bool = True,
    gs_views_interval: int = 1,
    inv_opacity: Optional[bool] = True,
    prune_by_depth_percent: Optional[float] = 1.0,
    prune_border_gs: Optional[bool] = True,
    match_3dgs_mcmc_dev: Optional[bool] = False,
):
    b = gaussians.means.shape[0]
    assert b == 1, "must set batch_size=1 when exporting 3D gaussians"
    src_v, out_h, out_w, _ = ctx_depth.shape

    # extract gs params
    world_means = gaussians.means
    world_shs = gaussians.harmonics
    world_rotations = gaussians.rotations
    gs_scales = gaussians.scales
    gs_opacities = inverse_sigmoid(gaussians.opacities) if inv_opacity else gaussians.opacities

    # Create a mask to filter the Gaussians.

    # TODO: prune the sky region here

    # throw away Gaussians at the borders, since they're generally of lower quality.
    if prune_border_gs:
        mask = torch.zeros_like(ctx_depth, dtype=torch.bool)
        gstrim_h = int(8 / 256 * out_h)
        gstrim_w = int(8 / 256 * out_w)
        mask[:, gstrim_h:-gstrim_h, gstrim_w:-gstrim_w, :] = 1
    else:
        mask = torch.ones_like(ctx_depth, dtype=torch.bool)

    # trim the far away point based on depth;
    if prune_by_depth_percent is not None and prune_by_depth_percent < 1:
        in_depths = ctx_depth
        d_percentile = torch.quantile(
            in_depths.view(in_depths.shape[0], -1), q=prune_by_depth_percent, dim=1
        ).view(-1, 1, 1)
        d_mask = (in_depths[..., 0] <= d_percentile).unsqueeze(-1)
        mask = mask & d_mask
    mask = mask.squeeze(-1)  # v h w

    # helper fn, must place after mask
    def trim_select_reshape(element):
        selected_element = rearrange(
            element[0], "(v h w) ... -> v h w ...", v=src_v, h=out_h, w=out_w
        )
        selected_element = selected_element[::gs_views_interval][mask[::gs_views_interval]]
        return selected_element

    export_ply(
        means=trim_select_reshape(world_means),
        scales=trim_select_reshape(gs_scales),
        rotations=trim_select_reshape(world_rotations),
        harmonics=trim_select_reshape(world_shs),
        opacities=trim_select_reshape(gs_opacities),
        path=Path(save_path),
        shift_and_scale=shift_and_scale,
        save_sh_dc_only=save_sh_dc_only,
        match_3dgs_mcmc_dev=match_3dgs_mcmc_dev,
    )