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opencc-by-4.0Jun 2024View details →

Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields

opencc-by-4.0Jun 2024View details →

Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"

opencc-by-4.0Jun 2024View details →

Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"

############# HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 ############# Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a> Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a> -------------------------------- <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <div>The overall structure of the data is:</div> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a>                                - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a>                     - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a>         - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a>                                  - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a>                   - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a>       - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> If you find our code, weights, predictions or ideas useful, please cite: @inproceedings{qi2024hoisdf,   title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},   author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},   pages={10392--10402},   year={2024} }

opencc-by-4.0Jan 2022View details →

Dataset of the study "Thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer: a cross-sectional study"

Data was collected to study the usefulness of the thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer, which is a cross-sectional study derived from the ONCORE project (Randomized controlled trial on comprehensive exercise-based cardiac rehabilitation program for the prevention of anthracyclines and/or anti-HER2 antibodies-induced cardiotoxicity in breast cancer), ClinicalTrials.gov Identifier: NCT03964142   DATASET FILE (xlsx) includes 4 sheets: - Dataset_variables: all variables collected pre-post intervention - Descriptive data (baseline): variables used for the descriptive analysis before the intervention (baseline) - Data_CPET-30STS(pre-post): pooled data from CPET-30STS pre-post intervention - Data_6MWD-30STS(pre-post): pooled data from 6MWD-30STS pre-post intervention

opencc-zeroOct 2022View details →

Distance estimation in the Goldfish (Carassius auratus)

Neurophysiological advances have given us exciting insights into the systems responsible for spatial mapping in mammals. However, we are still lacking information on the evolution of these systems and whether the underlying mechanisms identified are universal across phyla, or specific to the species studied. Here we address these questions by exploring whether a species that is evolutionarily distant from mammals can perform a task central to mammalian spatial mapping – distance estimation. We developed a behavioural paradigm allowing us to test whether goldfish (Carassius auratus) can estimate distance and explored the behavioural mechanisms that underpin this ability. Fish were trained to swim a set distance within a narrow tank covered with striped pattern. After changing the background pattern, we found that goldfish use the spatial frequency of their visual environment to estimate distance; doubling the spatial frequency of the background pattern resulted in a large overestimation of the swimming distance. These results provide robust evidence that goldfish can accurately estimate distance, and show that they use local optic flow to do so. These results provide a compelling basis to utilise goldfish as a model system to interrogate the evolution of the mechanisms that underpin spatial cognition, from brain to behaviour.

opencc-by-4.0Dec 2022View details →

IEEE 802.15.4 TSCH dataset for phase-based distance estimation

Introduction This data set contains two collections of phase angle measurements created in two indoor and one outdoor environment that can be used for phase-based distance estimates. The measurements include phase samples created on two different frequency sets: <ul> <li> TSCH standard frequencies: measurements are performed on default 16 channel frequencies {2405.0, 2410.0, 2415.0, 2420.0, 2425.0, 2430.0, 2435.0, 2440.0, 2445.0, 2450.0, 2455.0, 2460.0, 2465.0, 2470.0, 2474.0, 2480.0}MHz.</li> <li> Golomb ruler frequencies: the measurements are performed on 15 custom selected frequencies according to the Golomb ruler technique {2400.5, 2406.0, 2407.5, 2408.0, 2412.5, 2423.0, 2431.0, 2442.5, 2452.0, 2460.5, 2463.0, 2466.5, 2476.5, 2479.5, 2480.5}MHz.</li> </ul> Measurement setup Measurements were performed using AT86RF233 transceivers connected to the in-house <a href="https://log-a-tec.eu/hw-vesna.html">VESNA</a> platform. Two nodes were placed on a stand 1.6 m above the ground in three separate environments: <ul> <li>in a 5x5m square office with no furniture</li> <li>in an indoor hallway with dimensions of 4x40m</li> <li>in a park without any nearby obstacles</li> </ul> The actual distance between nodes was measured with a laser ranger with an accuracy of ±1.5 mm. There was no obstacle between the devices. Indoors, 17 WiFi access points were in operation during the measurement campaign. Phase measurement process The devices involved first establish an IEEE 802.15.4 TSCH network. In it, they measure the phase difference on pre-selected frequencies. The phase measurement has been seamlessly integrated into a communication so that the devices obtain phase measurement with every packet sent. Why two collections? The set labelled "TSCH standard channels" contains phase measurements created at frequencies defined in the IEEE.802.15.4 standard for the 2.4 GHz band. The frequency step (\(\Delta freq = freq_{i+1} - freq_{i}\)) between two phase samples is equal to 5MHz, which results in a maximum distinguishable range of 30m for the distance estimation. To increase the range up to 300m, the frequency step must be reduced to 0.5 MHz. This requires 160 phase samples in the 2.4 GHz band used with a bandwidth of 80 MHz. However, measuring 160 phase samples on 160 frequencies would take a lot of time and therefore interfere with TSCH communications. One way to shorten the procedure is to use the Golomb ruler technique. This allows a large set of phase differences to be created from a small number of measured phases. This method was used in the creation of the set named "Golomb ruler frequencies". The data set also contains a Python example script that expands the set of 15 measured frequencies to a set of 160 samples. Folder structure Each record collection is stored in a corresponding folder. Each folder contains .json files representing different environments. In addition to the data sets, the folders also contain figures and a sample Python script. The measurements are stored in JSON format. Each measured distance contains the number of measurements and the actual data. With each packet sent (identified by its Absolute Slot Number (ASN)), the phase difference between the devices is measured. The phase value is stored as an 8-bit value representing the range from 0 to 2 pi.

opencc-by-nc-nd-4.0Jul 2021View details →

Estimated stand-off distance between ADS-B equipped aircraft and obstacles

Summary: Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.   Description: For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft. For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the <a href="https://doi.org/10.2514/1.D0091">foundational research</a> used to support the <a href="https://doi.org/10.1520/F3442_F3442M-20">ASTM F3442/F3442M-20</a> well clear criteria of 2000 feet laterally and 250 feet AGL vertically. The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/releases/tag/v1.3">v1.3 uncorrelated encounter models</a> and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.” Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF. The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge. It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle. For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands <a href="https://adds-faa.opendata.arcgis.com/datasets/4d8fa46181aa470d809776c57a8ab1f6_0/about">open dataset</a>. Aircraft type was identified as part of the <a href="https://github.com/Airspace-Encounter-Models/em-processing-opensky">em-processing-opensky</a> workflow. <ul> <li>All: No filter, all observations that satisfied encounter conditions</li> <li>nearRunway: Aircraft within or at 2 nautical miles of a runway</li> <li>awayRunway: Observations more than 2 nautical miles from a runway</li> <li>glider: Observations when aircraft type is a glider</li> <li>fwme: Observations when aircraft type is a fixed-wing multi-engine</li> <li>fwse: Observations when aircraft type is a fixed-wing single engine</li> <li>rotorcraft: Observations when aircraft type is a rotorcraft</li> </ul> License This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0). This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA. MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized. As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license. Distribution Statement DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. © 2021 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work. This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001.  Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration. This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties.  Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein.  Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation.  Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information.  The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages.  The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein. Download and Format: The dataset is provided as a single .zip archive with multiple directories. The directories indicate the aircraft track dataset and types of obstacles. Within each directory are csv files corresponding to different the filtering criteria. These files are the total counts of observations (histogram) where columns corresponding to lateral (range) distance between the simulated obstacle and aircraft and rows correspond to the relative vertical separation. The files centers_x_ft.csv and centers_y_ft.csv are the center of each bin. Simply download the .zip file and extract. The MD5 checksum of the .zip file prior to uploading to Zenodo was 03e5aed725a4cf393683730e05521ba5. MIT Lincoln Laboratory LL Group and Division this dataset is associated with: <ul> <li>Group 42 / Division 4</li> <li>Topic: collision avoidance</li> <li>R&D Area: Air Traffic Control</li> <li>R&D Group: Surveillance Systems</li> </ul>

publicOct 2022View details →

Distance estimation in the Goldfish (Carassius auratus)

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publicOct 2025View details →

Estimating waiting distances between genealogy changes under a multi-species extension of the sequentially Markov coalescent

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publicSep 2025View details →

Visual circuitry for distance estimation in Drosophila

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opencc-by-4.0Jan 2021View details →

Distance estimates for "Gaia Early Data Release 3: The Galactic anticentre"

Distance estimates used by "Gaia Early Data Release 3: The Galactic anticentre", Gaia Collaboration, Antoja et al. These are Bayesian distances with an iterative prior, referred to as dPM within the paper (Appendix C). A parallax zero-point correction of 17µas has been applied before calculation. Any use of these distances should cite that paper.

opencc-by-4.0Feb 2022View details →

Isolation-by-distance and genetic parentage analysis provide similar larval dispersal estimates

An R studio project that includes original SNP data files used to quantify dispersal in Elacatinus lori via the isolation-by-distance (IBD) method. Associated R-code used to generate IBD regression slopes, calculate sigma, and construct dispersal kernels. Includes output from NeEstimator,  estimating effective population size.  Folders 1-3 contain the code/data needed to obtain the slope of the IBD relationship, effective population size, and the standard deviation (sigma) of the dispersal distribution, respectively. Folder 4 contains the R code needed to construct Laplacian dispersal kernels.

opencc-by-4.0Jun 2018View details →

Simple distance estimates for Gaia DR2 stars with radial velocities

Bayesian distance estimates for stars with radial velocities and parallaxes published in Gaia DR2. Our method and prior is designed to apply to this specific subset of stars in Gaia DR2. The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426 The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548

opencc-by-sa-4.0Sep 2024View details →

Magnetic Distance Estimation Data from Gait Experiments with Magnetoelectric Sensors

<h2>Overview</h2> This is the "Magnetic Distance Estimation Data from Gait Experiemtns with Magnetoelectric Sensors" dataset.  It represents a pilot study on magnetic motion tracking with novel magnetoelectric sensors during treadmill walking. Therefore, it contains both technical (calibration) data and clinical (gait) data of five healthy participants. Example scripts for loading and processing data are available in the linked respository. The dataset is formatted according to the Brain Imaging Data Structure. See the `dataset_description.json` file for the specific version used. The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. The data was recorded in the project B9 on "Magnetoelectric Sensors for Movement Detection and Analysis". All measurements were approved by the ethics committee of Kiel University (File number: A122/20) and conducted in accordance with the Declaration of Helsinki. <h2> Details about the experiment</h2> Magnetic motion tracking enables a relative tracking, in which the distance between each sensor and actuator node can be estimated.  The full setup contains two actuator nodes (a0, a1) and four sensor nodes (s0, s1, s2, s3). Each actuator-sensor pair produces nine magnetic signals (x,y,z by x,y,z) as well as three magnetic dipole moment signals that represent the currents through the coils (actuators). Additionally, each node was tracked with an optical motion capture (OMC) system. The resulting position and orientation data of the attached rigid body act as a reference (ground truth) to evaluate the magnetic estimation.  Subfolders sub-01 to sub-08 each contain up to three calibration tasks which each contain between 60 and 120s of arbitrary movement of one coil (wand-mounted) around one stationary sensor (base). Subfolders sub-09 to sub-13 each contain two 120s walking tasks (0.5 and 1 m/s) of five subjects in total with the full sensor and actuator setup. Two actuators were mounted to the shanks, two sensors to the thighs and two sensors were placed stationary next to the threadmill.  The dataset also contains a folder with derived data, which contains calibration parameters for each actuator-sensor pair. See the provided matlab script for details on how to load, visualize, and compare the results.

publicJun 2022View details →

Spatial uncertainty in herbarium data: Simulated displacement but not error distance alters estimates of phenological sensitivity to climate in a widespread California wildflower

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publicAug 2025View details →

Using isolation-by-distance to jointly estimate effective population density and dispersal distance: a practical evaluation using bumble bees

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publicApr 2025View details →

Wildlife density estimation by distance sampling: A novel technique with movement compensation

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