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Adrián Peñate Sánchez

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2 papers

1 author row

IROS Conference 2017 Conference Paper

Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images

Lorenzo Porzi
Adrián Peñate Sánchez
Elisa Ricci 0001
Francesc Moreno-Noguer

Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier-typically a random forest-to predict the position of each input pixel on the object surface. These estimates are then used to define an energy function that is minimized w. r. t. the object pose. In this paper, we focus on the first stage of the problem and propose a novel classifier based on a depth-aware Convolutional Neural Network. This classifier is able to learn a scale-adaptive regression model that yields very accurate pixel-level predictions, allowing to finally estimate the pose using a simple RANSAC-based scheme, with no need to optimize complex ad hoc energy functions. Our experiments on publicly available datasets show that our approach achieves remarkable improvements over state-of-the-art methods.

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ICRA Conference 2015 Conference Paper

Efficient monocular pose estimation for complex 3D models

Antonio Rubio 0001
Michael Villamizar
Luis Ferraz
Adrián Peñate Sánchez
Arnau Ramisa
Edgar Simo-Serra
Alberto Sanfeliu
Francesc Moreno-Noguer

We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.

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