Roberto Javier López-Sastre

Learn More
Deformable Part Models (DPMs) as introduced by Felzenszwalb et al. have shown remarkably good results for category-level object detection. In this paper, we explore whether they are also well suited for the related problem of category-level object pose estimation. To this end, we extend the original DPM so as to improve its accuracy in object category pose(More)
This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a(More)
Simultaneous object detection and pose estimation is a challenging task in computer vision. In this paper, we tackle the problem using Hough Forests. Unlike most methods in the literature, we focus on the problem of continuous pose estimation. Moreover, we aim for a probabilistic output. We first introduce a new pose purity criterion for splitting a node(More)
Generic 3D shape retrieval is a fundamental research area in the field of content-based 3D model retrieval. The aim of this track is to measure and compare the performance of generic 3D shape retrieval methods implemented by different participants over the world. The track is based on a new generic 3D shape benchmark, which contains 1200 triangle meshes(More)
This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification(More)
We present a novel method for constructing a visual vocabulary that takes into account the class labels of images, thus resulting in better recognition performance and more efficient learning. Our method consists of two stages: Cluster Precision Maximisation (CPM) and Adaptive Refinement. In the first stage, a Reciprocal Nearest Neighbours (RNN) clustering(More)
This paper focuses on the problem of 3D shape categorization. For a given set of training 3D shapes, a 3D shape recognition system must be able to predict the class label for a test 3D shape. We introduce a novel discriminative approach for recognizing 3D shape categories which is based on a 3D Spatial Pyramid (3DSP) decomposition. 3D local descriptors(More)
This paper presents a novel approach for accelerating the popular Reciprocal Nearest Neighbors (RNN) clustering algorithm, i.e. the fast-RNN. We speed up the nearest neighbor chains construction via a novel dynamic slicing strategy for the projection search paradigm. We detail an efficient implementation of the clustering algorithm along with a novel data(More)