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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
import numpy as np
import torch
from gsplat.rendering import rasterization
import kiui
class GaussianRenderer:
def __init__(self, opt):
self.opt = opt
self.gs_view_chunk_size = self.opt.get('gs_view_chunk_size', 1)
def render(self, gaussians, cam_view, bg_color=None, intrinsics=None):
# gaussians: [B, N, 14]
# cam_pos: [B, V, 3]
B, V = cam_view.shape[:2]
# loop of loop...
images, alphas, depths = [], [], []
for b in range(B):
for v in range(0, V, self.gs_view_chunk_size):
# pos, opacity, scale, rotation, shs
means3D = gaussians[b, :, 0:3].contiguous().float()
opacity = gaussians[b, :, 3:4].contiguous().float()
scales = gaussians[b, :, 4:7].contiguous().float()
rotations = gaussians[b, :, 7:11].contiguous().float()
rgbs = gaussians[b, :, 11:].contiguous().float() # [N, 3]
# render novel views
view_matrix = cam_view[b, v:v+self.gs_view_chunk_size].float()
V_sub = view_matrix.shape[0]
viewmat = view_matrix.transpose(2, 1) # [V, 4, 4]
view_intrinsics = intrinsics[b, v: v+self.gs_view_chunk_size]
Ks = [torch.tensor([[view_intrinsic[0],0.,view_intrinsic[2]],[0.,view_intrinsic[1],view_intrinsic[3]],[0., 0., 1.]],dtype=means3D.dtype, device=means3D.device) for view_intrinsic in view_intrinsics]
rendered_image_all, rendered_alpha_all, info = rasterization(
means=means3D,
quats=rotations,
scales=scales,
opacities=opacity.squeeze(-1),
colors=rgbs,
viewmats=viewmat,
Ks=torch.stack(Ks),
width=self.opt.img_size[1],
height=self.opt.img_size[0],
near_plane=self.opt.znear,
far_plane=self.opt.zfar,
packed=False,
backgrounds=torch.stack([bg_color for _ in range(V_sub)]) if bg_color is not None else None,
render_mode="RGB+ED",
)
for rendered_image, rendered_alpha in zip(rendered_image_all, rendered_alpha_all):
depths.append(rendered_image[...,3:].permute(2, 0, 1))
rendered_image = rendered_image[...,:3].permute(2, 0, 1)
images.append(rendered_image)
alphas.append(rendered_alpha.permute(2, 0, 1))
images, alphas, depths = torch.stack(images), torch.stack(alphas), torch.stack(depths)
images, alphas, depths = images.view(B, V, *images.shape[1:]), alphas.view(B, V, *alphas.shape[1:]), depths.view(B, V, *depths.shape[1:])
return {
"images_pred": images, # [B, V, 3, H, W]
"alphas_pred": alphas, # [B, V, 1, H, W]
"depths_pred": depths, # [B, V, 1, H, W]
}
def save_ply(self, gaussians, path, compatible=True):
# gaussians: [B, N, 14]
# compatible: save pre-activated gaussians as in the original paper
assert gaussians.shape[0] == 1, 'only support batch size 1'
from plyfile import PlyData, PlyElement
means3D = gaussians[0, :, 0:3].contiguous().float()
opacity = gaussians[0, :, 3:4].contiguous().float()
scales = gaussians[0, :, 4:7].contiguous().float()
rotations = gaussians[0, :, 7:11].contiguous().float()
shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float() # [N, 1, 3]
# prune by opacity
mask = opacity.squeeze(-1) >= 0.005
means3D = means3D[mask]
opacity = opacity[mask]
scales = scales[mask]
rotations = rotations[mask]
shs = shs[mask]
# invert activation to make it compatible with the original ply format
if compatible:
opacity = kiui.op.inverse_sigmoid(opacity)
scales = torch.log(scales + 1e-8)
shs = (shs - 0.5) / 0.28209479177387814
xyzs = means3D.detach().cpu().numpy()
f_dc = shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
opacities = opacity.detach().cpu().numpy()
scales = scales.detach().cpu().numpy()
rotations = rotations.detach().cpu().numpy()
l = ['x', 'y', 'z']
# All channels except the 3 DC
for i in range(f_dc.shape[1]):
l.append('f_dc_{}'.format(i))
l.append('opacity')
for i in range(scales.shape[1]):
l.append('scale_{}'.format(i))
for i in range(rotations.shape[1]):
l.append('rot_{}'.format(i))
dtype_full = [(attribute, 'f4') for attribute in l]
elements = np.empty(xyzs.shape[0], dtype=dtype_full)
attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, 'vertex')
PlyData([el]).write(path)
def load_ply(self, path, compatible=True):
from plyfile import PlyData, PlyElement
plydata = PlyData.read(path)
xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
np.asarray(plydata.elements[0]["y"]),
np.asarray(plydata.elements[0]["z"])), axis=1)
print("Number of points at loading : ", xyz.shape[0])
opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
shs = np.zeros((xyz.shape[0], 3))
shs[:, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
shs[:, 1] = np.asarray(plydata.elements[0]["f_dc_1"])
shs[:, 2] = np.asarray(plydata.elements[0]["f_dc_2"])
scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
scales = np.zeros((xyz.shape[0], len(scale_names)))
for idx, attr_name in enumerate(scale_names):
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot_")]
rots = np.zeros((xyz.shape[0], len(rot_names)))
for idx, attr_name in enumerate(rot_names):
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
gaussians = np.concatenate([xyz, opacities, scales, rots, shs], axis=1)
gaussians = torch.from_numpy(gaussians).float() # cpu
if compatible:
gaussians[..., 3:4] = torch.sigmoid(gaussians[..., 3:4])
gaussians[..., 4:7] = torch.exp(gaussians[..., 4:7])
gaussians[..., 11:] = 0.28209479177387814 * gaussians[..., 11:] + 0.5
return gaussians |