Surface Normals Estimation

24 papers with code • 6 benchmarks • 9 datasets

Surface normal estimation deals with the task of predicting the surface orientation of the objects present inside a scene. Refer to Designing Deep Networks for Surface Normal Estimation (Wang et al.) to get a good overview of several design choices that led to the development of a CNN-based surface normal estimator.

Most implemented papers


DLR-RM/BlenderProc 25 Oct 2019

BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks.

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations

DrSleep/multi-task-refinenet 13 Sep 2018

Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards.

Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

leoshine/Spherical_Regression CVPR 2019

We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation.

Deep Iterative Surface Normal Estimation

nnaisense/deep-iterative-surface-normal-estimation CVPR 2020

This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.

$360^o$ Surface Regression with a Hyper-Sphere Loss

VCL3D/SphericalViewSynthesis 16 Sep 2019

We present a dataset of $360^o$ images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 surface estimation.

Creative Flow+ Dataset

creativefloworg/creativeflow CVPR 2019

We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth.

Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction

StevenHickson/CreateNormals 16 Jun 2019

We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.

ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

Shreeyak/cleargrasp 6 Oct 2019

To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.

DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

sitzikbs/DeepFit ECCV 2020

We propose a surface fitting method for unstructured 3D point clouds.