Spacetime stereo: a unifying framework for depth from triangulation

By James Davis, Ravi Ramamoorthi, Szymon Rusinkiewicz. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

301 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/56f38548dfca230808d727965270e83d7b4888d3

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

High-accuracy stereo depth maps using structured light (2003, background): “While doing our research, we became aware of concurrent work aimed at acquiring high-quality correspondences with active illumination that is applicable to both static and dynamic scenes [8, 24].”
Unique Signatures of Histograms for Local Surface Description (2010, methodology): “Finally, as a practical application in a challenging and active research area, we demonstrate the use of SHOT correspondences to perform fully automatic 3D Reconstruction from Spacetime Stereo data.”
Efficiently combining positions and normals for precise 3D geometry (2005, methodology): “To acquire positions, we use a temporal stereo triangulation scanner [Zhang et al. 2003; Davis et al. 2005], which combines the accuracy of active triangulation with the ease of calibration of stereo.”
Spacetime stereo: shape recovery for dynamic scenes (2003, methodology): “[7] develop a similar spacetime stereo framework as the one presented here.”
Recent progresses on real-time 3D shape measurement using digital fringe projection techniques (2010, methodology): “Spacetime stereo is another technique that has potential for high speed 3D shape measurement [2] [3] [4].”
High-speed 3D shape measurement with structured light methods: A review (2018, methodology): “Spacetime stereo is another technique that has potential for high speed 3D shape measurement [42–44] .”
SHOT: Unique signatures of histograms for surface and texture description (2014, methodology): “In the evaluation proposed in [49], focused on object recognition in clutter and occlusion from CAD models, (a) (b) (c) (d) Figure 10: 3D Reconstruction from Spacetime Stereo views (first two rows) and Kinect views (last two rows): (a) initial set of views (b) coarse registration (c) global registra”
An Application of Markov Random Fields to Range Sensing (2005, background): “Active stereo techniques supply their own light [4].”
Dynamic shape capture using multi-view photometric stereo (2009, methodology): “Real-time Structured Light Several methods capture dynamic facial performance geometry using structured illumination, usually emitted from a video projector onto the subject, and observed from a single viewpoint [Rusinkiewicz et al. 2002; Davis et al. 2005; Zhang et al. 2004; Zhang and Huang 2006].”
High-resolution, real-time three-dimensional shape measurement (2006, unknown): “and Davis et al.(3) developed a new concept called spacetime”
Discriminative learning of Markov random fields for segmentation of 3D scan data (2005, methodology): “The dataset was acquired by a scanning system based on temporal stereo [4].”
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth (2014, background): “Establishing ground-truth disparities from the input views directly avoids the calibration problem and can be done via unstructured light [1, 6, 34], but only yields disparities for nonoccluded scene points visible in both input images.”
Fast 3D Scanning with Automatic Motion Compensation (2007, background): “The authors of [20] and [5] present recent systems combining structured light and stereo into a common framework.”
Unique shape context for 3d data description (2010, methodology): “The other dataset, referred to as Lab, consists of scenes and models acquired in our laboratory by means of a 3D sensing technique known as Spacetime Stereo [6], [21].”
Dynamic 3-D shape measurement method: A review (2010, methodology): “Several 3-D shape measurement methods based on 2-D fringe projected, including moiré technique (MT) [24–27], phase measuring profilometry (PMP) [28–32], Fourier transformation profilometry (FTP) [33–37], modulation measurement profilometry (MMP) [38–40], spatial phase detection (SPD) [41–43], color-”