3D AI: Meshes, NeRFs, and Point Clouds

3D Representations

Text-to-3D Generate 3D models from text descriptions:

# Conceptual API (e.g., Meshy, Tripo3D)
model_3d = generate_3d(
    prompt="a detailed medieval sword with gold handle",
    format="glb",
    resolution="high"
)

Image-to-3D

# Generate 3D from single image
from zero123pp import Zero123PP

model = Zero123PP.load()
mesh = model.generate_mesh(
    image="input.png",
    num_views=16
)
mesh.export("output.glb")

NeRF (Neural Radiance Fields) Train a neural network to represent a 3D scene:

# nerfstudio example
# 1. Capture images from multiple angles
# 2. Run COLMAP for camera poses
# 3. Train NeRF

ns-train nerfacto
    --data ./images
    --output-dir ./output

3D Gaussian Splatting Faster alternative to NeRF:

# Requires camera poses + images
# Train in minutes, render in real-time
python train.py
    -s ./scene
    --iterations 30000

Tools and Platforms

Point Cloud Processing

import open3d as o3d

# Load point cloud
pcd = o3d.io.read_point_cloud("scan.ply")

# Downsample
pcd_down = pcd.voxel_down_sample(voxel_size=0.05)

# Normal estimation
pcd.estimate_normals()

# Surface reconstruction
mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd)

Use Cases

Challenges

• Quality still behind manual modeling
• Limited control over output
• Texture/material quality
• Animation support limited