3D Vision & Graphics Research

核心能力

触发场景

• "解释3D Gaussian Splatting的核心原理"
• "NeRF和3DGS有什么区别"
• "如何训练自己的3DGS模型"
• "推荐3D重建数据集"
• "点云配准用什么算法"
• "如何在Blender中渲染点云"
• "InstantNGP的加速原理"
• "SuGaR如何从3DGS提取网格"
• "3D视觉领域最新SOTA是什么"
• "Mip-NeRF 360的贡献在哪里"

算法知识库

神经辐射场 (NeRF) 家族

3D Gaussian Splatting (3DGS) 家族

点云处理

网格处理

数据集全览

多视图/场景级

物体/实例级

人脸/人体

工具链指南

Blender Python API 常用操作

import bpy
import numpy as np

# 导入点云
def import_pointcloud(points, colors=None):
    mesh = bpy.data.meshes.new("pointcloud")
    obj = bpy.data.objects.new("pointcloud", mesh)
    bpy.context.collection.objects.link(obj)
    mesh.from_pydata(points.tolist(), [], [])
    if colors is not None:
        color_attr = mesh.color_attributes.new(
            name="Col", type="FLOAT_COLOR", domain="POINT"
        )
        for i, color in enumerate(colors):
            color_attr.data[i].color = color.tolist() + [1.0]

# 导入3DGS PLY
def import_3dgs_ply(filepath):
    from plyfile import PlyData
    plydata = PlyData.read(filepath)
    verts = plydata['vertex']
    xyz = np.stack([verts['x'], verts['y'], verts['z']], axis=-1)
    # 创建球体实例表示每个高斯
    for i, pos in enumerate(xyz):
        bpy.ops.mesh.primitive_ico_sphere_add(
            subdivisions=1, radius=0.01, location=pos
        )

# 渲染设置
def setup_cycles_render(samples=128, resolution=(1920, 1080)):
    bpy.context.scene.render.engine = 'CYCLES'
    bpy.context.scene.cycles.samples = samples
    bpy.context.scene.render.resolution_x = resolution[0]
    bpy.context.scene.render.resolution_y = resolution[1]
    bpy.context.scene.cycles.device = 'GPU'

Open3D 点云处理

import open3d as o3d
import numpy as np

# 读取与可视化
pcd = o3d.io.read_point_cloud("scene.ply")
o3d.visualization.draw_geometries([pcd])

# 降采样
pcd_down = pcd.voxel_down_sample(voxel_size=0.02)

# 法线估计
pcd.estimate_normals(
    o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)
)

# ICP配准
reg_p2p = o3d.pipelines.registration.registration_icp(
    source, target, max_correspondence_distance=0.05,
    init=np.eye(4),
    estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint()
)

# 泊松重建
mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
    pcd, depth=9
)

# 半径离群点去除
cl, ind = pcd.remove_radius_outlier(nb_points=16, radius=0.05)
pcd_clean = pcd.select_by_index(ind)

# DBSCAN聚类
labels = np.array(pcd.cluster_dbscan(eps=0.02, min_points=10))

常见问题排查

注意事项

1. 学术用途需遵守数据集许可协议
2. 3DGS相关代码需要CUDA 11.6+和合适的GPU
3. 商业项目注意区分开源(Apache/MIT)与Research Only许可
4. 大场景训练建议至少24GB显存
5. 点云处理建议使用Open3D 0.18+
6. Blender脚本注意bpy在headless模式下需要特殊配置

定价

¥0.50/次，使用支付宝AI收协议。每次调用提供完整的论文解读/算法对比/代码指导/实验设计方案。