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Patrik Huber

3 accepted papers

2023

Neural Implicit Surface Reconstruction from Noisy Camera Observations (Student Abstract)

AAAI 2023technical

Representing 3D objects and scenes with neural radiance fields has become very popular over the last years. Recently, surface-based representations have been proposed, that allow to reconstruct 3D objects from simple photographs. However, most current techniques require an accurate camera calibratio…

Cited by 0SourcePDFScholar
2018

Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural Networks

CVPR 2018poster

We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs). We first compare and analyse different loss functions including L2, L1 and smooth L1. The analysis of these loss functions suggests that, for the training of a CNN-bas…

Cited by 548SourcePDFScholar
2017

Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting

CVPR 2017poster

We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces. Our DAC-CSR divides facial landmark detection into three cascaded sub-tasks: face bounding box refinement, general CSR and at…

Cited by 120PDFScholar