Surface Normals and Curvature Estimation for Noisy Point Cloud Data
-
Updated
Sep 8, 2022 - Python
Surface Normals and Curvature Estimation for Noisy Point Cloud Data
Estimating Ground Surface Normals and Fitting Surfaces to Noisy LIDAR Point Cloud Data
Estimate surface normals from a single image; 3-stack-hourglass Architecture using Tensorflow; Reduce the Mean Angle Error down to 0.4397164234
A multimodal UAV assistant dataset.
Normal Inference Module in PyTorch, IROS 2020
GenPercept: Diffusion Models Trained with Large Data Are Transferable Visual Models
Differentiable Point-based Inverse Rendering
Term project. A python implementation of the Basic Photometric Stereo Algorithm
Official code for FOUND: Foot Optimisation with Uncertain Normals for Surface Deformation using Synthetic Data
Code accompanying the paper "360 Surface Regression with a Hyper-Sphere Loss", 3DV 2019
[BMVC 2022] IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty
Tools accompanying the 3D60 spherical panoramas dataset
[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
SNE-RoadSeg for Freespace Detection in PyTorch, ECCV 2020
Add a description, image, and links to the surface-normals topic page so that developers can more easily learn about it.
To associate your repository with the surface-normals topic, visit your repo's landing page and select "manage topics."