Roberto Javier López-Sastre

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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)
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)
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)
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)
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)
Object category detection has received a lot of attention over the last decades. Recently, several approaches have gone one step further proposing solutions for the problem of simultaneous object category detection and pose estimation [1, 3, 5]. In this paper, we tackle this problem using Hough Forests (HF) [2]. We propose a new approach (see Figure 1)(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)
The challenging problem that we explore in this paper is to precisely estimate the number of vehicles in an image of a traffic congestion situation. We start introducing TRANCOS, a novel database for extremely overlapping vehicle counting. It provides more than 1200 images where the number of vehicles and their locations have been annotated. We establish a(More)