Browse SoTA > Computer Vision > Surface Normals Estimation

# Surface Normals Estimation Edit

6 papers with code · Computer Vision

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# BlenderProc

25 Oct 2019DLR-RM/BlenderProc

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

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

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

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# ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

6 Oct 2019Shreeyak/cleargrasp

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.

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# Robust Learning Through Cross-Task Consistency

7 Jun 2020EPFL-VILAB/XTConsistency

Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.

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# Robust Learning Through Cross-Task Consistency

Visual perception entails solving a wide set of tasks (e. g., object detection, depth estimation, etc).

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

16 Sep 2019VCL3D/SphericalViewSynthesis

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.

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