Beginner Explanation
Technical Explanation
Academic Context
3D avatar reconstruction is a significant area of research in computer vision, often linked to topics such as photogrammetry, multi-view geometry, and neural rendering. Key mathematical foundations include projective geometry and optimization techniques for fitting 3D models to observed data. Notable papers in this field include ‘Multi-View 3D Object Reconstruction’ by Hartley and Zisserman, which discusses the principles of reconstructing 3D structures from multiple 2D views, and ‘Learning Monocular 3D Object Reconstruction’ by Choy et al., which explores deep learning approaches for single-image 3D reconstruction. These works highlight the evolution of techniques from traditional geometry-based methods to modern data-driven approaches.Code Examples
Example 1:
import cv2
import numpy as np
# Load two images
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')
# Convert to grayscale
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
# Compute disparity map
stereo = cv2.StereoBM_create()
disparity = stereo.compute(gray1, gray2)
# Display the disparity map
cv2.imshow('Disparity', disparity)
cv2.waitKey(0)
cv2.destroyAllWindows()Example 2:
import cv2
import numpy as np
# Load two images
img1 = cv2.imread('image1.jpg')Example 3:
import numpy as np
# Load two images
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')View Source: https://arxiv.org/abs/2511.16673v1