3D-color video camera

Rubinstein, O.; Honen, Yaron; Bronstein, Alexander M.; Bronstein, Michael M.; Kimmel, Ron

doi:10.1109/iccvw.2009.5457433

Cited by 11 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Driven by some academic prototypes building low-cost depth cameras based on coded light technology [ 24 , 25 , 26 ], Intel ® RealSense™ launched the world smallest consumer depth camera, the F200. Later, Intel ® proposed an improved version, the SR300 model [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

Curto

Araújo

2022

Sensors

View full text Add to dashboard Cite

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.

show abstract

Section: Related Workmentioning

confidence: 99%

An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

Curto

Araújo

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…We are convinced that such fields can offer interesting solutions to pattern recognition problems, if we are able to encode our problem in biological terms. A very recent and interesting example of such an alternative way of thinking is the Video Genome Project 2 , where internet videos were encoded as "video DNA sequences" and analysed with phylogenetic related tools [7].…”

Section: Introductionmentioning

confidence: 99%

2D Shapes Classification Using BLAST

Lovato

Bicego

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a novel 2D shape classification approach, which exploits in this context the huge amount of work carried out by bioinformaticians in the biological sequence analysis research field. In particular, in the approach presented here, we propose to encode shapes as biological sequences, employing the widely known sequence alignment tool called BLAST (Basic Local Alignment Search Tool) to devise a similarity score, used in a nearest neighbour scenario. Obtained results on standard datasets show the feasibility of the proposed approach.

show abstract