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# -----------------------------------------------------------------------------
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# -----------------------------------------------------------------------------
import argparse
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle as pkl
import plotly.graph_objs as go
import plotly.io as pio
from tqdm import tqdm
matplotlib.use("agg")
class Pose():
"""
A class of operations on camera poses (numpy arrays with shape [...,3,4]).
Each [3,4] camera pose takes the form of [R|t].
"""
def __call__(self, R=None, t=None):
"""
Construct a camera pose from the given rotation matrix R and/or translation vector t.
Args:
R: Rotation matrix [...,3,3] or None
t: Translation vector [...,3] or None
Returns:
pose: Camera pose matrix [...,3,4]
"""
assert R is not None or t is not None
if R is None:
if not isinstance(t, np.ndarray):
t = np.array(t)
R = np.eye(3, device=t.device).repeat(*t.shape[:-1], 1, 1)
elif t is None:
if not isinstance(R, np.ndarray):
R = np.array(R)
t = np.zeros(R.shape[:-1], device=R.device)
else:
if not isinstance(R, np.ndarray):
R = np.array(R)
if not isinstance(t, np.ndarray):
t = np.tensor(t)
assert R.shape[:-1] == t.shape and R.shape[-2:] == (3, 3)
R = R.astype(np.float32)
t = t.astype(np.float32)
pose = np.concatenate([R, t[..., None]], axis=-1) # [...,3,4]
assert pose.shape[-2:] == (3, 4)
return pose
def invert(self, pose, use_inverse=False):
"""
Invert a camera pose.
Args:
pose: Camera pose matrix [...,3,4]
use_inverse: Whether to use matrix inverse instead of transpose
Returns:
pose_inv: Inverted camera pose matrix [...,3,4]
"""
R, t = pose[..., :3], pose[..., 3:]
R_inv = R.inverse() if use_inverse else R.transpose(0, 2, 1)
t_inv = (-R_inv @ t)[..., 0]
pose_inv = self(R=R_inv, t=t_inv)
return pose_inv
def compose(self, pose_list):
"""
Compose a sequence of poses together.
pose_new(x) = poseN o ... o pose2 o pose1(x)
Args:
pose_list: List of camera poses to compose
Returns:
pose_new: Composed camera pose
"""
pose_new = pose_list[0]
for pose in pose_list[1:]:
pose_new = self.compose_pair(pose_new, pose)
return pose_new
def compose_pair(self, pose_a, pose_b):
"""
Compose two poses together.
pose_new(x) = pose_b o pose_a(x)
Args:
pose_a: First camera pose
pose_b: Second camera pose
Returns:
pose_new: Composed camera pose
"""
R_a, t_a = pose_a[..., :3], pose_a[..., 3:]
R_b, t_b = pose_b[..., :3], pose_b[..., 3:]
R_new = R_b @ R_a
t_new = (R_b @ t_a + t_b)[..., 0]
pose_new = self(R=R_new, t=t_new)
return pose_new
def scale_center(self, pose, scale):
"""
Scale the camera center from the origin.
0 = R@c+t --> c = -R^T@t (camera center in world coordinates)
0 = R@(sc)+t' --> t' = -R@(sc) = -R@(-R^T@st) = st
Args:
pose: Camera pose to scale
scale: Scale factor
Returns:
pose_new: Scaled camera pose
"""
R, t = pose[..., :3], pose[..., 3:]
pose_new = np.concatenate([R, t * scale], axis=-1)
return pose_new
def to_hom(X):
"""Get homogeneous coordinates of the input by appending ones."""
X_hom = np.concatenate([X, np.ones_like(X[..., :1])], axis=-1)
return X_hom
def cam2world(X, pose):
"""Transform points from camera to world coordinates."""
X_hom = to_hom(X)
pose_inv = Pose().invert(pose)
return X_hom @ pose_inv.transpose(0, 2, 1)
def get_camera_mesh(pose, depth=1):
"""
Create a camera mesh visualization.
Args:
pose: Camera pose matrix
depth: Size of the camera frustum
Returns:
vertices: Camera mesh vertices
faces: Camera mesh faces
wireframe: Camera wireframe vertices
"""
vertices = np.array([[-0.5, -0.5, 1],
[0.5, -0.5, 1],
[0.5, 0.5, 1],
[-0.5, 0.5, 1],
[0, 0, 0]]) * depth # [6,3]
faces = np.array([[0, 1, 2],
[0, 2, 3],
[0, 1, 4],
[1, 2, 4],
[2, 3, 4],
[3, 0, 4]]) # [6,3]
vertices = cam2world(vertices[None], pose) # [N,6,3]
wireframe = vertices[:, [0, 1, 2, 3, 0, 4, 1, 2, 4, 3]] # [N,10,3]
return vertices, faces, wireframe
def merge_xyz_indicators_plotly(xyz):
"""Merge xyz coordinate indicators for plotly visualization."""
xyz = xyz[:, [[-1, 0], [-1, 1], [-1, 2]]] # [N,3,2,3]
xyz_0, xyz_1 = unbind_np(xyz, axis=2) # [N,3,3]
xyz_dummy = xyz_0 * np.nan
xyz_merged = np.stack([xyz_0, xyz_1, xyz_dummy], axis=2) # [N,3,3,3]
xyz_merged = xyz_merged.reshape(-1, 3)
return xyz_merged
def get_xyz_indicators(pose, length=0.1):
"""Get xyz coordinate axis indicators for a camera pose."""
xyz = np.eye(4, 3)[None] * length
xyz = cam2world(xyz, pose)
return xyz
def merge_wireframes_plotly(wireframe):
"""Merge camera wireframes for plotly visualization."""
wf_dummy = wireframe[:, :1] * np.nan
wireframe_merged = np.concatenate([wireframe, wf_dummy], axis=1).reshape(-1, 3)
return wireframe_merged
def merge_meshes(vertices, faces):
"""Merge multiple camera meshes into a single mesh."""
mesh_N, vertex_N = vertices.shape[:2]
faces_merged = np.concatenate([faces + i * vertex_N for i in range(mesh_N)], axis=0)
vertices_merged = vertices.reshape(-1, vertices.shape[-1])
return vertices_merged, faces_merged
def unbind_np(array, axis=0):
"""Split numpy array along specified axis into list."""
if axis == 0:
return [array[i, :] for i in range(array.shape[0])]
elif axis == 1 or (len(array.shape) == 2 and axis == -1):
return [array[:, j] for j in range(array.shape[1])]
elif axis == 2 or (len(array.shape) == 3 and axis == -1):
return [array[:, :, j] for j in range(array.shape[2])]
else:
raise ValueError("Invalid axis. Use 0 for rows or 1 for columns.")
def plotly_visualize_pose(poses, vis_depth=0.5, xyz_length=0.5, center_size=2, xyz_width=5, mesh_opacity=0.05):
"""
Create plotly visualization traces for camera poses.
Args:
poses: Camera poses to visualize [N,3,4]
vis_depth: Size of camera frustum visualization
xyz_length: Length of coordinate axis indicators
center_size: Size of camera center markers
xyz_width: Width of coordinate axis lines
mesh_opacity: Opacity of camera frustum mesh
Returns:
plotly_traces: List of plotly visualization traces
"""
N = len(poses)
centers_cam = np.zeros([N, 1, 3])
centers_world = cam2world(centers_cam, poses)
centers_world = centers_world[:, 0]
# Get the camera wireframes.
vertices, faces, wireframe = get_camera_mesh(poses, depth=vis_depth)
xyz = get_xyz_indicators(poses, length=xyz_length)
vertices_merged, faces_merged = merge_meshes(vertices, faces)
wireframe_merged = merge_wireframes_plotly(wireframe)
xyz_merged = merge_xyz_indicators_plotly(xyz)
# Break up (x,y,z) coordinates.
wireframe_x, wireframe_y, wireframe_z = unbind_np(wireframe_merged, axis=-1)
xyz_x, xyz_y, xyz_z = unbind_np(xyz_merged, axis=-1)
centers_x, centers_y, centers_z = unbind_np(centers_world, axis=-1)
vertices_x, vertices_y, vertices_z = unbind_np(vertices_merged, axis=-1)
# Set the color map for the camera trajectory and the xyz indicators.
color_map = plt.get_cmap("gist_rainbow") # red -> yellow -> green -> blue -> purple
center_color = []
faces_merged_color = []
wireframe_color = []
xyz_color = []
x_color, y_color, z_color = *np.eye(3).T,
for i in range(N):
# Set the camera pose colors (with a smooth gradient color map).
r, g, b, _ = color_map(i / (N - 1))
rgb = np.array([r, g, b]) * 0.8
wireframe_color += [rgb] * 11
center_color += [rgb]
faces_merged_color += [rgb] * 6
xyz_color += [x_color] * 3 + [y_color] * 3 + [z_color] * 3
# Plot in plotly.
plotly_traces = [
go.Scatter3d(x=wireframe_x, y=wireframe_y, z=wireframe_z, mode="lines",
line=dict(color=wireframe_color, width=1)),
go.Scatter3d(x=xyz_x, y=xyz_y, z=xyz_z, mode="lines", line=dict(color=xyz_color, width=xyz_width)),
go.Scatter3d(x=centers_x, y=centers_y, z=centers_z, mode="markers",
marker=dict(color=center_color, size=center_size, opacity=1)),
go.Mesh3d(x=vertices_x, y=vertices_y, z=vertices_z,
i=[f[0] for f in faces_merged], j=[f[1] for f in faces_merged], k=[f[2] for f in faces_merged],
facecolor=faces_merged_color, opacity=mesh_opacity),
]
return plotly_traces
def write_html(poses, file, dset, vis_depth=1, xyz_length=0.2, center_size=0.01, xyz_width=2):
"""Write camera pose visualization to HTML file."""
if dset == "lightspeed":
xyz_length = xyz_length / 3
xyz_width = xyz_width
vis_depth = vis_depth / 3
center_size *= 3
traces_poses = plotly_visualize_pose(poses, vis_depth=vis_depth, xyz_length=xyz_length,
center_size=center_size, xyz_width=xyz_width, mesh_opacity=0.05)
traces_all2 = traces_poses
layout2 = go.Layout(scene=dict(xaxis=dict(visible=False), yaxis=dict(visible=False), zaxis=dict(visible=False),
dragmode="orbit", aspectratio=dict(x=1, y=1, z=1), aspectmode="data"),
height=400, width=600, showlegend=False)
fig2 = go.Figure(data=traces_all2, layout=layout2)
html_str2 = pio.to_html(fig2, full_html=False)
file.write(html_str2)
def viz_poses(i, seq, file, dset, dset_parent):
"""Visualize camera poses for a sequence and write to HTML file."""
if "/" in seq:
seq = "_".join(seq.split("/"))
if "mp4" in seq:
seq = seq[:-4]
file.write(f"<span style='font-size: 18pt;'>{i} {seq}</span><br>")
if dset == "dynpose_100k":
with open(f"{dset_parent}/cameras/{seq}.pkl", "rb") as f:
poses = pkl.load(f)["poses"]
else:
with open(f"{dset_parent}/poses.pkl", "rb") as f:
poses = pkl.load(f)[seq]
write_html(poses, file, dset)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dset", type=str, default="dynpose_100k", choices=["dynpose_100k", "lightspeed"])
parser.add_argument("--dset_parent", type=str, default=".")
args = parser.parse_args()
outdir = f"pose_viz/{args.dset}"
os.makedirs(outdir, exist_ok=True)
split_size = 6 # to avoid poses disappearing
viz_list = f"{args.dset_parent}/viz_list.txt"
seqs = open(viz_list).read().split()
for j in tqdm(range(int(np.ceil(len(seqs)/split_size)))):
with open(f"{outdir}/index_{str(j)}.html", "w") as file:
for i, seq in enumerate(tqdm(seqs[j*split_size:j*split_size+split_size])):
viz_poses(i, seq, file, args.dset, args.dset_parent)
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