Surface Normals Estimation
33 papers with code • 8 benchmarks • 12 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.
Benchmarks
These leaderboards are used to track progress in Surface Normals Estimation
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Datasets
ScanNet
NYUv2
PASCAL Context
Taskonomy
IBims-1
GRIT
Stanford-ORB
3D Ken Burns Dataset
Pano3D
SuperCaustics
Latest papers with no code
Large-scale Monocular Depth Estimation in the Wild
To overcome this limitation, In this work, a new approach is proposed to accumulate Depth & Surface Normal datasets from the world of different Video Games in an easy and reproducible way.
Neural-PBIR Reconstruction of Shape, Material, and Illumination
In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination.
CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision
Meanwhile, SANE easily establishes multi-task learning with CORE loss functions on both depth and surface normal estimation, leading to the whole performance leap.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images.
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation
Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes.
