nuscenes 点云 显示

Nuscenes是一个开源的自动驾驶数据集，其中包含了来自纽约市和新加坡的高分辨率3D点云数据。点云数据可以使用多种方式进行显示，包括：

1. 通过使用点云可视化软件（如Potree）来加载和显示点云数据。这种方法可以让用户轻松地浏览点云，并进行基本的交互操作（如旋转、缩放、平移等）。

2. 使用Python库（如Matplotlib和Mayavi）加载和显示点云数据。这种方法可以用于在Python环境中进行更高级的数据分析和可视化。

3. 通过使用现成的点云可视化工具（如CloudCompare和MeshLab）来加载和显示点云数据。这种方法通常需要一些额外的配置和调整，但可以提供更多的可视化选项和功能。

总之，点云数据的显示可以通过多种方法进行，具体取决于用户的需求和技术水平。

相关问题

nuscenes 点云 框 显示

您可以使用nuscenes库中的代码来显示nuscenes点云框。以下是一些示例代码：

1. 在nuscenes可视化工具中显示点云框

from nuscenes.nuscenes import NuScenes
from nuscenes.utils.data_classes import LidarPointCloud
from nuscenes.utils.geometry_utils import BoxVisibility, transform_matrix

nusc = NuScenes(version='v1.0-mini', dataroot='/data/nuscenes', verbose=True)

# Get a lidar point cloud and transform it to the ego vehicle frame.
sample_rec = nusc.sample[0]
lidar_token = sample_rec['data']['LIDAR_TOP']
lidar_data = nusc.get('sample_data', lidar_token)
pc = LidarPointCloud.from_file(nusc.get_sample_data_path(lidar_token))
ego_pose = nusc.get('ego_pose', lidar_data['ego_pose_token'])
calib = nusc.get('calibrated_sensor', lidar_data['calibrated_sensor_token'])
lidar_points = pc.points[:3, :]  # Take the first 3 rows because the fourth row is reflectance.

# Transform the point cloud to the ego vehicle frame.
lidar_points = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ lidar_points
lidar_points = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ lidar_points

# Get the annotations for the first sample.
annotations = nusc.get('sample_annotation', sample_rec['anns'])

# Set up the visualization.
vis = nuscenes.utils.visualizations.NuscenesVisualizer(nusc=nusc)

# Loop over the annotations and add them to the visualization.
for i, ann in enumerate(annotations):
    # Only show boxes that are visible from the lidar point cloud.
    if not BoxVisibility.isVisible(ann['bbox'], lidar_points):
        continue
    # Get the box corners and transform them to the ego vehicle frame.
    corners = nuscenes.utils.geometry_utils.get_3d_box(ann, nusc)
    corners = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ corners
    corners = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ corners
    # Add the box to the visualization.
    vis.add_box(corners, label=ann['category_name'], color='red', alpha=0.5)

# Render the visualization.
vis.render()

2. 使用matplotlib库显示点云框

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

# Get the annotations for the first sample.
annotations = nusc.get('sample_annotation', sample_rec['anns'])

# Set up the plot.
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim3d(-50, 50)
ax.set_ylim3d(-50, 50)
ax.set_zlim3d(-5, 5)

# Loop over the annotations and add them to the plot.
for i, ann in enumerate(annotations):
    # Only show boxes that are visible from the lidar point cloud.
    if not BoxVisibility.isVisible(ann['bbox'], lidar_points):
        continue
    # Get the box corners and transform them to the ego vehicle frame.
    corners = nuscenes.utils.geometry_utils.get_3d_box(ann, nusc)
    corners = transform_matrix(ego_pose['translation'], ego_pose['rotation'], inverse=False) @ corners
    corners = transform_matrix(calib['translation'], calib['rotation'], inverse=False) @ corners
    # Add the box to the plot.
    facecolor = 'red'
    edgecolor = 'black'
    alpha = 0.5
    collection = Poly3DCollection([corners.T[:4], corners.T[4:8], [corners.T[0], corners.T[4], corners.T[5], corners.T[1]], [corners.T[1], corners.T[5], corners.T[6], corners.T[2]], [corners.T[2], corners.T[6], corners.T[7], corners.T[3]], [corners.T[3], corners.T[7], corners.T[4], corners.T[0]]], alpha=alpha, facecolor=facecolor, edgecolor=edgecolor)
    ax.add_collection(collection)

# Plot the lidar points.
ax.scatter(lidar_points[0], lidar_points[1], lidar_points[2], s=0.1)

# Show the plot.
plt.show()

这些示例代码可以帮助您显示nuscenes点云框。

polarstream

PolarStream是一种流式激光雷达（Lidar）目标检测和分割模型，由Qi Chen、Sourabh Vora和Oscar Beijbom等人提出。该模型将Lidar作为固有的流式数据源，并通过在楔形点云扇区上运行而不是完整点云来减少计算量和内存占用。与以笛卡尔坐标系表示扇区的方法不同，PolarStream采用极坐标系表示扇区，并引入了多尺度填充和特征不变性等关键改进。在NUSCENES数据集上的实验结果显示，PolarStream相较于基于流的其他方法有显着的改进，并且与现有的非流定制方法相当。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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