In this video, I use the same simulation, but accelerate all particles towards the center of a sphere.
okreylos
Quick follow-up to my previous video, "Why Does The Vacuum Of Space Not Suck Away Our Atmosphere," youtube.com/watch?v=vwk4mSFFop0 .
In this video, I use the same simulation, but accelerate all particles towards the center of a sphere.
In this video, I use the same simulation, but accelerate all particles towards the center of a sphere.
updated 6 years ago
In this video, I use the same simulation, but accelerate all particles towards the center of a sphere.
0:00 Intro, finger tracking
0:40 3D navigation (translation, rotation)
2:00 3D navigation (scaling)
5:25 GUI interaction (menus)
7:20 GUI interaction (dialog windows)
10:50 GUI interaction (scale bar)
13:00 Dynamic tools (creation)
16:16 Dynamic tools (destruction)
17:40 Dynamic tools (saving input graphs)
18:05 Text entry with QuikWriting
20:06 Recap, outro
Vrui project page: https://web.cs.ucdavis.edu/~okreylos/ResDev/Vrui
Nanotech Construction Kit: https://web.cs.ucdavis.edu/~okreylos/ResDev/NanoTech
Proper video showing off the box and all the newest AR Sandbox features coming soon.
AR Sandbox project page: https://web.cs.ucdavis.edu/~okreylos/ResDev/SARndbox
AR Sandbox support forum: http://doc-ok.org/?forum=ar-sandbox-forum
The target in frame is 20 meters away; the out-of-frame target is 15 meters away.
Before I get angry comments: ;-)
- My Valve Index's built-in microphone is still recording total silence. No idea why.
- I know one doesn't "shoot" arrows, but "looses" them. It slipped out.
- I changed the quiver position right before filming; should have practiced first.
Important note: at 8:57 I say that averaging to remove noise is a bad idea, but I forgot to mention the most important reason why: averaging can get rid of *random* noise, but this noise *is not random, as I explain.*
While a VR application is running, the virtual camera position can be set at any time simply by touching a real camera's lens with an input device and pressing a selected button. From that point on, the real camera will show a precisely aligned view of the virtual world.
Victoria Keddie's web site: http://www.victoriakeddie.com
Space-Track.org's web site: space-track.org
en.wikipedia.org/wiki/2022_Hunga_Tonga_eruption_and_tsunami
Yesterday's video: youtube.com/watch?v=POVVzwkQ2KE
One More Orbit's web site: onemoreorbit.com
Here is the article about the flight I reference in the video: cnn.com/travel/article/pole-to-pole-pan-am-flight-50/index.html
Smithsonian navigation page mentioned in the video: https://timeandnavigation.si.edu/multimedia-asset/great-circle-route
More information:
http://www.idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://www.idav.ucdavis.edu/~okreylos/ResDev/Collaboration
http://modlab.ucdavis.edu
Here's a related video showing the same simulation around a globe, with no side walls: youtu.be/TyVx3UnZbVU
Referenced video: youtube.com/watch?v=RK93TfSYeQU
I filmed this (using a mobile phone; I apologize for poor audio and video quality) to test tracked-camera video recording during a filming session we did in the UC Davis ModLab for an upcoming 90 minute National Geographic TV special on Mars exploration.
The Mars rover CAD model was provided by the Jet Propulsion Laboratory (JPL).
The 3D Mars terrain data was reconstructed from images sent back to Earth by the rover's NavCam stereo cameras. The rover CAD model is "parked" exactly where the rover actually stood on Mars while capturing the initial set of source imagery.
The terrain texture was created by projecting images sent back to Earth by the rover's MastCam color camera onto the reconstructed 3D terrain.
Dawn is viewing the 3D rover model and the 3D Mars terrain model using an HTC Vive virtual reality headset, running custom VR visualization software developed at UC Davis.
More information:
nasa.gov/mission_pages/msl/index.html
mars.nasa.gov/msl/mission/rover/eyesandother
https://geology.ucdavis.edu/people/faculty/sumner
twitter.com/sumnerd
http://modlab.ucdavis.edu
This time, we unveiled the new "VR mode," where users wearing VR headsets can explore the dynamic sand surface in real time and at 1:1 scale.
The small red sphere on the TV display indicates the position of the VR user inside the AR Sandbox. In this clip, a group of users around the AR Sandbox created a high-altitude reservoir, and the VR user wants to experience a dam failure from ground level.
More information: http://idav.ucdavis.edu/~okreylos/ResDev/SARndbox
Simulation data kindly provided by Prof. Toby Allen, formerly of UC Davis, now at RMIT University, Melbourne, Australia.
Related videos I mention in the video:
"Behind the Curve" on Netflix: netflix.com/title/81015076
YouTube video where I found the photograph: "Mt San Jacinto from 123 miles in High Res IR on super clear day!", youtube.com/watch?v=KxLwaaU1aNk
This work was inspired by the following recent publication: Parger, M., Schmalstieg, D., Mueller, J.H., and Steinberger, M., "Human Upper-Body Inverse Kinematics for Increased Embodiment in Consumer-Grade Virtual Reality," Symposium on Virtual Reality Software and Technology (VRST '18), November 28-December 1, 2018, Tokyo, Japan. ACM, New York, NY, USA, 10 pages. doi.org/10.1145/3281505.3281529
This video is part of a set-up guide for the Vrui VR development toolkit, "Set-up Instructions for Vrui with HTC Vive Head-mounted Display," http://doc-ok.org/?p=1737
More information:
Vrui: http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
ProtoShop is a modeling program for proteins, specifically designed to create initial configurations for ab-initio protein structure prediction. It's described in detail in this paper, which won the "Best Application" award at the 2003 IEEE Visualization conference:
Kreylos, O., Max, N.L., Hamann, B., Crivelli, S.N. and Bethel, E.W., "Interactive Protein Manipulation," in: Turk, G., van Wijk, J.J., and Moorhead, R.J., eds., "IEEE Visualization 2003," IEEE Computer Society Press, Los Alamitos, CA, pp. 581-588
ProtoShop has been used in several international protein structure prediction competitions, and was crucial in predicting the structures of several large proteins that were too complex for non-interactive prediction.
More information on (VR) ProtoShop: http://idav.ucdavis.edu/~okreylos/ResDev/ProtoShop
This video shows a visualization of the methane gas cloud reconstructed from airborne measurements, collected by a small plane between 1:30pm and 3:30pm on November 10th, 2015. The data collection was overseen by UC Davis researcher Dr. Ian Faloona.
The game and 3D rendering engine were developed from scratch, as an unholy mixture of C++ and 386 assembly for the low-level parts such as fixed-point linear algebra, 2D frame buffer graphics, and multi-source sound mixing.
More information: http://doc-ok.org/?p=1690
The LiDAR data were collected by UC San Francisco under an ARRA grant, and the aerial images were collected under the National Agricultural Imaging program.
The LiDAR scan contains approximately 1 billion 3D points.
More information: http://idav.ucdavis.edu/~okreylos/ResDev/LiDAR
The LiDAR Data were collected by Dr. Gerald Bawden of the US Geological Survey.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/LiDAR
The dataset is a 2008 model created by Dr. Richard Allen, UC Berkeley Seismolab. While the data have been made obsolete by newer models integrating more source data, this one is still a good illustration of 3D Visualizer's principle of operation.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/DataExploration
http://seismo.berkeley.edu
This video shows a simulation of a moving and rotating object in two dimensions, tracked by an external absolute measurement system and a relative measurement system integrated into the tracked object. Measurements from these two systems are combined using a non-linear extension of the Kalman filter, yielding a result with low noise, low update latency, and no drift.
Related videos:
Pure IMU-based Positional Tracking is a No-go: youtube.com/watch?v=_q_8d0E3tDk
Optical 3D Pose Estimation of Oculus Rift DK2: youtube.com/watch?v=X4G6_zt1qKY
Lighthouse Tracking Examined - Headset at Rest: youtube.com/watch?v=Uzv2H3PDPDg
Lighthouse Tracking Examined - Headset in Motion: youtube.com/watch?v=XwxwMruEE7Y
Lighthouse Tracking Examined - Controller in Motion: youtube.com/watch?v=A75uKqA67FI
Playstation Move Tracking Test: youtube.com/watch?v=0J5LaWykiIU
More information:
en.wikipedia.org/wiki/Kalman_filter
This specific test was performed on a 28" 3840x2160 LCD monitor, viewed from a distance of 31.5", with 16x multisampling.
More information:
http://doc-ok.org/?p=1631
More information:
http://doc-ok.org/?p=1623
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
This video was entirely created and recorded in-engine, without green screens or any post-processing.
More information:
http://doc-ok.org/?p=1623
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
The calibration procedure is very similar, only that now the camera needs to be aimed at the calibration disk from a variety of positions and orientations.
More information:
http://doc-ok.org/?p=1623
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
A flat disk (a CD/DVD covered in paper on both sides) is attached to a controller or other tracked object, and shown to the camera in several different positions, and several different orientations, throughout the tracked space. As tie points are collected, the software updates the tracker-camera calibration in real time, and finally saves it as the camera's persistent extrinsic calibration.
More information:
http://doc-ok.org/?p=1623
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
On second thought, this is probably more like playing catch in pass-through augmented reality, but whatever.
More information:
http://doc-ok.org/?p=1508
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
Featuring a visitor stomping through a high-resolution LiDAR scan of downtown San Francisco and environs, displayed here at 1:500 scale.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev
http://idav.ucdavis.edu/~okreylos/ResDev/LiDAR
http://doc-ok.org/?p=1508
http://doc-ok.org/?p=432
Featuring KeckCAVES post-doc Carlye Peterson interacting with a model of carbon isotope distribution in the global ocean created by Jake Gebbie at Woods Hole Oceanographic Institution.
More information:
http://doc-ok.org/?p=1508
http://idav.ucdavis.edu/~okreylos/ResDev
The visualization software shown in this clip is 3D Visualizer, looking at combinations of 3D isotope field reconstructions superimposed over a high-resolution global bathymetry model. Collaboration between individual VR systems (in this case CAVE and Vive) is supported by the Vrui VR toolkit's collaboration infrastructure add-on. Remote users are embedded into each VR system as real-time, pseudo-holographic 3D video avatars captured by one or more 3D cameras integrated into each VR system.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Collaboration
http://idav.ucdavis.edu/~okreylos/ResDev/DataExploration
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/DataExploration
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://doc-ok.org/?p=1508
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://doc-ok.org/?p=1508
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://doc-ok.org/?p=1508
Source article: http://doc-ok.org/?p=1478
Source article: http://doc-ok.org/?p=1478
Source article: http://doc-ok.org/?p=1478
Correction: I made a mistake at 0:55. The controllers are not connected to the host PC via Bluetooth, but via a custom 2.4 GHz radio protocol routed through the headset.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/SARndbox
http://arsandbox.org
So here's the reason behind the AR Sandbox's default 1s topography update delay, explained once and for all.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/SARndbox
http://arsandbox.org
The algorithm is based on blob extraction, and fitting a cone with apex at the camera's center of projection to the boundary of the extracted blob to envelop the PS Move's glowing sphere.
Cone fitting appears to have low noise, and be robust against occlusion.
Algorithm details: http://doc-ok.org/?p=1599
Related video: Sensor Fusion for Object Tracking, youtube.com/watch?v=-nsylEpgVek
Filmed with a standard video camera and no post-processing of any kind. Everything visible in the video was visible to the real person in the CAVE.
Chair is for scale.
More information:
http://idav.ucdavis.edu/~okreylos/ResDev/Kinect
http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
http://idav.ucdavis.edu/~okreylos/ResDev/KeckCAVES
This presentation looks at the fundamentals of locomotion (or navigation) in virtual reality, presents a framework of treating real and virtual locomotion uniformly, and examines how the same framework can be employed to address important issues caused by locomotion, such as simulator sickness and boundary violations.
KeckCAVES web site: http://www.keckcaves.org
Vrui web page: http://idav.ucdavis.edu/~okreylos/ResDev/Vrui
Doc Ok's blog: http://doc-ok.org


