3D modeling from uncalibrated images
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Updated
Dec 10, 2021 - Python
3D modeling from uncalibrated images
[ICCV 2023] When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo
Implementing different steps to estimate the 3D motion of the camera. Provides as output a plot of the trajectory of the camera.
[BMVC2021] "TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation"
Real-Time Monocular Visual SLAM with Pose-graph optimization
Real-time Stereo Visual SLAM Pipeline with Bundle Adjustment
Programs to detect keyPoints in Images using SIFT, compute Homography and stitch images to create a Panorama and compute epilines and depth map between stereo images.
3D scene reconstruction and camera pose estimation given images from different views (Structure from Motion)
This is an implementation of Shearlet Transform (ST) for light field reconstruction using TensorFlow 1.x.
simple library for uncalibrated stereo rectification using feature points
Might be considered an easier version, since the React frontend has been replaced with a PyQt frontend, and the no-cv-sfm branch is used.
Structure from Motion and NeRF
Depth Estimation Using Stereo Cameras
Some projects are modified from Chu-Song Chen's class of 3D Computer Vision with Deep Learning Applications at National Taiwan University.
3D scene reconstruction (stereo)
3D reconstruction using SfM
Scalable multi-view diffusion model with flexible viewpoint generation using epipolar geometry to improve multi-view consistency in 3D reconstruction (CVPRW 2024).
Simple Structure From Motion pipeline from scratch
Project to find disparity and depth maps for given two image sequences of a subject
CMSC733 - Pipeline to reconstruct a 3D scene and simultaneously obtain the camera poses of a monocular camera w.r.t. the given scene
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