We present a portable device to capture both shape and reflectance of an indoor scene. Consisting of a Kinect, an IR camera and several IR LEDs, our device allows the user to acquire data in a similar way as he she scans with a single Kinect. Scene geometry is reconstructed by KinectFusion. To estimate reflectance from incomplete and noisy observations, 3D vertices of the same material are identified by our material segmentation propagation algorithm. Then BRDF observations at these vertices are merged into a more complete and accurate BRDF for the material. Effectiveness of our device is demonstrated by quality results on real-world scenes.
4. @cite_6 required a user-specified number of materials, which is hard for average users to decide, while our system does not require additional input from the user.
@cite_6
2110930073 2122778642 2141175968 1978868011
This paper addresses the problem of how to rank retrieval systems without the need for human relevance judgments, which are very resource intensive to obtain. Using TREC 3, 6, 7 and 8 data, it is shown how the overlap structure between the search results of multiple systems can be used to infer relative performance differences. In particular, the overlap structures for random groupings of five systems are computed, so that each system is selected an equal number of times. It is shown that the average percentage of a system's documents that are only found by it and no other systems is strongly and negatively correlated with its retrieval performance effectiveness, such as its mean average precision or precision at 1000. The presented method uses the degree of consensus or agreement a retrieval system can generate to infer its quality. This paper also addresses the question of how many documents in a ranked list need to be examined to be able to rank the systems. It is shown that the overlap structure of the top 50 documents can be used to rank the systems, often producing the best results. The presented method significantly improves upon previous attempts to rank retrieval systems without the need for human relevance judgments. This ''structure of overlap'' method can be of value to communities that need to identify the best experts or rank them, but do not have the resources to evaluate the experts' recommendations, since it does not require knowledge about the domain being searched or the information being requested. When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5 to 10 , respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work. Relevance judgments are used to compare text retrieval systems. Given a collection of documents and queries, and a set of systems being compared, a standard approach to forming judgments is to manually examine all documents that are highly ranked by any of the systems. However, not all of these relevance judgments provide the same benefit to the final result, particularly if the aim is to identify which systems are best, rather than to fully order them. In this paper we propose new experimental methodologies that can significantly reduce the volume of judgments required in system comparisons. Using rank-biased precision, a recently proposed effectiveness measure, we show that judging around 200 documents for each of 50 queries in a TREC-scale system evaluation containing over 100 runs is sufficient to identify the best systems. An analysis of three major problems in the application of bibliometric research performance indicators is made in three separate sections. In the first section, the influence of field-dependent citation practices is analysed. The results indicate that rankings of publications from different fields, based on citation counts, can be affected seriously by differences between citation characteristics in those fields. If certain assumptions hold, one should expect high (short term) citation levels in Biochemistry, Celbiology and Biophysics. Medium citation levels are to be expected in Experimental and Molecular Physics, Physical and Organic Chemistry, Pharmacology and Plant Physiology, and low citation levels in Mathematics, Taxonomy, Pharmacognosy and Inorganic Solid State Chemistry. In the second section time-dependent factors are studied. It is shown that trend-analyses of output and impact based on bibliometric scores can be disturbed by changes in theSCI-database and in publication and citation practices. One of the disturbing factors is shown to be the inclusion of so called Books into theSCI data-base in 1977. Finally, in the third section a case is presented which illustrates the consequences of operating on incomplete bibliometric data in the evaluation of scientific performance. A completeness percentage of 99 for publication data is proposed as a standard in evaluations of the performance of small university research groups).