Comparison of a few different methods for estimating surface normals in a point cloud.
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Updated
Mar 10, 2024 - Jupyter Notebook
Comparison of a few different methods for estimating surface normals in a point cloud.
CVPR 2024: Robust Depth Enhancement via Polarization Prompt Fusion Tuning
tcensReg is a package written to obtain maximum likelihood estimates from a truncated normal distribution with censoring.
Code for our Building Facades to Normal Maps – Adversarial Learning from Single View Images work accepted at CRV 2021
Normal estimation based on dual camera input
MSCV 2019 Capstone Project
Reconstruct mesh from point cloud data generated by 3D scanner
Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
Official GitHub repo for VecKM. A very efficient and descriptive local geometry encoder / point tokenizer / patch embedder. ICML2024.
(BMVC 2020 Oral) Neighbourhood-Insensitive Point Cloud Normal Estimation Network
nyuv2 toolbox for data extraction and loading.
Facial Depth and Normal Estimation using Dual-Pixel Camera (ECCV 22)
A novel fast approximate least squares normal estimator using the structural information of certain LiDAR, is fast and accurate compared to PCL, and meets the real-time requirements of the LIO system.
DIODE Development Toolkit
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation
[CVPR 2020] Normal Assisted Stereo Depth Estimation
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)
Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral & Best paper finalist)
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