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43 results for “Multi-view”
Dataset for "Decomposing God Header File via Multi-View Graph Clustering"
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Millimeter-wave Multi-View Radar (MMVR) Dataset
<p><strong>Introduction</strong></p> <p>The Millimeter-wave Multi-View Radar (MMVR) dataset comprises 345K multi-view radar frames collected from 25 human subjects over 6 different rooms. It includes 446K annotated bounding boxes/segmentation instances, and 7.59 million annotated keypoints to support three perception tasks: object detection, pose estimation, and instance segmentation in the image plane.</p> <p>MMVR consists of 35 data sessions, each stored in a data folder with the format d<em>x</em>sy, where <em>x</em> represents the day and y the session index of that day when the data were collected.</p> <p>Within each data folder, data frames are grouped into 395 non-overlapping one-minute data segments. Each data segment is stored in a folder named using a three-digit, zero-filled convention based on the chronological order. For example, the first one-minute data segment is saved in the folder 000, while the second one-minute data segment is stored in the folder 001.</p> <p>Within each data segment folder, each data frame consists of 5 NPZ files: meta, radar, bounding boxes (bbox), keypoints (pose) and segmentation masks (mask). Each data frame is named using a five-digit, zero-filled convention based on the chronological order within the one-minute data segment.</p> <pre><code>Root/ ├── d1s1/ │ └── ... ├── d1s2/ │ ├── 000/ │ │ ├── 00000_meta.npz ... Meta info │ │ ├── 00000_radar.npz ... Horizontal/Vertical heatmaps │ │ ├── 00000_bbox.npz ... 2D Bounding Boxes │ │ ├── 00000_pose.npz ... 2D keypoints │ │ ├── 00000_mask.npz ... 2D Segmentation masks │ │ ├── 00001_meta.npz │ │ ├── 00001_radar.npz │ │ ├── 00001_bbox.npz │ │ ├── 00001_pose.npz │ │ ├── 00001_mask.npz │ │ . │ │ ├── 00899_meta.npz │ │ ├── 00899_radar.npz │ │ ├── 00899_bbox.npz │ │ ├── 00899_pose.npz │ │ └── 00899_mask.npz │ └── 001/ │ ├── 00000_meta.npz │ ├── 00000_radar.npz │ ├── 00000_bbox.npz │ ├── 00000_pose.npz │ ├── 00000_mask.npz . └── ... ├── d9s5/ │ └── ... └── d9s6/ └── ... </code></pre> <p> </p> <p><strong>At a Glance</strong></p> <ul> <li> <ul> <li>The size of the unzipped dataset is ~81.8 GB</li> <li>The MMVR dataset consists of 345K data frames with each frame including 5 NPZ files: meta, radar, bounding boxes (bbox), keypoints (pose) and segmentation masks (mask).</li> <li>The MMVR dataset is divided into four smaller chunks to facilitate easier downloads: three chunks of approximately 20 GB each and one chunk of 12 GB. <ul> <li>P1.zip (24.2 GB): <ul> <li>README.md and its related figure folder (figs)</li> <li>load_sample.ipynb: a python snippet to load and visualize a data frame</li> <li>data_split.npz: an npz file containing the predefined data splits (S1 or S2) for training, validation, and test sets of data segments under P1 and P2.</li> <li>all data frames under P1 (d1s1, d1s2, d2s2, d3s1, d3s2, d4s1)</li> </ul> </li> <li>P2_00.zip (20.4 GB): all data frames in d5s1 – d5s6 and d6s1 – d6s6</li> <li>P2_01.zip (21.6 GB): all data frames in d7s1 – d7s5 and d8s1 – d8s6</li> <li>P2_02.zip (11.7 GB): all data frames in d9s1 – d9s6</li> </ul> </li> </ul> </li> </ul> <p><strong>Citation</strong></p> <p>If you use the MMVR dataset in your research, please cite our contribution:</p> <pre><code>@inproceedings{MMVR2024, title={MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception}, author={M. Mahbubur Rahman and Ryoma Yataka and Sorachi Kato and Pu Perry Wang and Peizhao Li and Adriano Cardace and Petros Boufounos}, booktitle={Proceedings of European Conference on Computer Vision (ECCV)}, pages={}, year={2024} }</code></pre> <p><strong>License</strong></p> <p>The MMVR dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
raycast: multi-view object detection in UAV image clouds (case-study data)
<p>This package contains image data for conducting multi- and single-view sewer inlet detection in UAV image clouds. The package consists in: - individual UAV images, taken with high overlap and corrected for lens distortion - orthophoto of the case study area, clipped to road boundaries.</p>
Figure 8 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 8 Comparison of images taken with a Keyence VHX 5000 digital microscope (lens: Z20, A–C) and DISC3D (D). The whole specimen of Pogonocherus hispidus can be imaged at once with the VHX 5000 with a X30-magnification (A). To compare the digital resolution, we focus on the pronotum of the beetle (B: VHX 5000, ×30; C: VHX 5000, ×100, D: DISC3D, ×1.26). Scale bars: 1 mm.
Figure 7 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 7 Osmia adunca, two exemplary raw images of the front-light stack, with the focal plane going through the proximal (A) and the distal part (B) of the sample, the EDOF image (C), and a detail of the latter (D) demonstrate the resolution. Scale bars: 1 mm.
Figure 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 2 Illumination by two hemispherical white-coated domes (A–C). The back-light-dome can be removed for specimen mounting (D, E). No direct light from the LED-stripes hits the specimens (C, E).
Figure 17 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 17 Relation of surface area and volume for all insect species presented here. While the dipteran, hymenopteran and lepidopteran species had their wings unfolded, all beetles had the wings folded underneath their elytra. Models are not to scale.
Figure 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 3 The camera is mounted on a macro-rail (A). The camera position and orientation can be fine-tuned in all directions (B–D). The camera lens is covered by a pinhole-cap (B).
Figure 10 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 10 Comparison of dense-cloud-based mesh generation and visual consistency meshing of Thricops sp. (A EDOF image). Thin and delicate structure like wings and setae are not well modelled from the dense cloud (B) but well preserved by visual consistency meshing (C).
Figure 11 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 11 Comparison of mesh quality and number of polygons, exemplified by a model of the shell of Discus rotundatus. The model with 1 million faces (A) has a file size (3D-PDF) of 35 MB and shows more detail, but the reduced model with 75.000 faces (B) still well resembles the structure with a file size (3D-PDF) of only 3 MB.
Figure 9 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 9 Workflow of model generation of Pogonocherus hispidus with PhotoScan Pro. In total, 398 EDOF-images are taken with DISC3D (one example is shown in A). Using the image data, masks and camera positions estimated with the calibration sphere (see Fig. 6), a sparse cloud with optimized camera positions is generated (B). Two options for model generation are available: direct mesh calculation based on a dense point cloud (C) or meshing with visual consistency (D). Resulting meshes can be textured (E, F). Scale bar: 1mm.
Figure 12 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 12 Overview and size comparison of the specimens used in this study. Coleoptera: a Prosopocoilus savagei b Anoplotrupes stercorosus, *: specimen was broken during comparative measurements c Stenocorus meridianus d Typhaeus typhoeus e Rutpela maculata f Valgus hemipterus g Cryptocephalus sericeus h Pogonocherus hispidus i Phyllobius pyri j Tytthaspis sedecimpunctata; Lepidoptera: k Zygaena filipendulae; Hymenoptera: l Paraponera clavata m Osmia adunca n Sphecodes ephippius; Diptera: o Thricops sp., p Culex pipiens q Oscinella frit; Gastropoda: r Helicodonta obvoluta s Aegopinella nitens t Discus rotundatus.; Scale bar: 1 cm (keep in mind that not all specimens are equidistant to the lens; i.e., at the same height of the needle).
Figure 5 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 5 Workflow of image masking using front- and back-light information.
Figure 4 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 4 Workflow of EDOF-calculation. For a detailed description, see Suppl. material 1: S2.
Figure 18 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 18 Interactive, textured 3D-model of Anoplotrupes stercorosus (21 mm body size).
Figure 16 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 16 Interactive 3D-model of Helicodonta obvoluta (9 mm shell diameter).
Figure 15 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 15 Interactive 3D-model of Prosopocoilus savagei (23 mm body size).
Figure 14 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 14 Interactive 3D-model of Pogonocherus hispidus (6 mm body size).
Figure 13 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 13 Interactive 3D-model of Oscinella frit (1.5 mm body size).
Figure 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Figure 1 Schematic setup (A) and image (B) of the Darmstadt Insect Scanner DISC3D.
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International Brain Laboratory public data
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OpenNeuro
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