FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation DisneyResearchHub
ReNeRF:Relightable Neural Radiance Fields with Nearfield Lighting DisneyResearchHub
Environmental Digital Reconstruction https://blogbarley.blogspot.com/2024/08/environmental-digital-reconstruction.html
RACE Rapid Acceleration to Cybernetic Existence NOTES Noetic Terminal Escape Planet unification toward local system colonization https://blogbarley.blogspot.com/2026/10/race-notes-013b.html
..........
https://blogbarley.blogspot.com/2024/12/poker-beta-test.html
 |
| GIANTMONSTER gif |
Monte Carlo link h
https://www.ibm.com/topics/monte-carlo-simulation
Shiny object AI, lightweight Python
Smartphone to contact lens
Eye reflection/MIT RICE shiny object link https://openaccess.thecvf.com/content/CVPR2023/papers/Tiwary_ORCa_Glossy_Objects_As_Radiance-Field_Cameras_CVPR_2023_paper.pdf
Poker match
Eyeglasses, sunglasses, eyeball reflection utilized zoom
 |
| https://www.contactlensjournal.com/article/S1367-0484(21)00021-7/abstract |
Video m, n
Casino Royale (2006) - Final Poker Scene
DR. NO | "Bond, James Bond" – Sean Connery, Eunice Gayson | James Bond
Las Vegas casino test
SLAM for multiple extended targets using 6G, WIFI, & satellite signal
saline power system
example for ORCa:
slim Python embedded on lens microprocessor example:
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing Virtual Cones for case of sphere "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Objective\n",
"Given spherical surface , set of camera rays and camera points, find virtual cones for each camera ray in parallel.\n",
"\n",
"Starting with including required packaged and adding some helper functions"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget\n",
"import torch\n",
"from torch import autograd\n",
"from functools import partial\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import sys\n",
"sys.path.append('.')\n",
"from utils.viz_util import (plot3d_arrow, plot3d_sphere, \n",
" plot3d_point, plot3d_cone)\n",
"\n",
"dot = lambda x,y: (x*y).sum(-1, keepdim=True)\n",
"norm = lambda x: torch.sqrt((x**2).sum(-1, keepdim=True))\n",
"transp = lambda x: x.transpose(-2,-1)\n",
"normalize = lambda x: x/(1e-7+norm(x))\n",
"to_np = lambda x: x[0,0].detach().cpu().numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Given quantities\n",
"\n",
"$N$: Number of primary rays\n",
"\n",
"$P$: Number of intersection points per primary ray\n",
"\n",
"$\\mathbf{o}$: Primary ray origin ($N \\times P \\times 3$)\n",
"\n",
"$\\mathbf{d}$: Primary ray direction ($N\\times P \\times 3$)\n",
"\n",
"$t$: Distance along ray for intersection points($N\\times P \\times 1$) \n",
"\n",
"$r$: Radius of primary cone ($N \\times P \\times 3$)\n",
"\n",
"$\\mathbf{n}$: Surface normals at intersection normals ($N \\times P \\times 3$) \n",
"\n",
"$R$: Radius of curvature at the surface ($N \\times P \\times 3$)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Defining one primary ray,then repeating it to $N\\times P$ values to test out the performance of this module in ORCa. Overall code should work with $N$ different primary rays"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"N = 128\n",
"P = 192\n",
"\n",
"expand_NP = lambda x: x[None,None].expand(N,P,-1)\n",
"\n",
"# Example visualization\n",
"o = expand_NP(torch.cuda.FloatTensor([3.,0.,0.])) # (N,P,3)\n",
"d = expand_NP(normalize(torch.cuda.FloatTensor([-1,0.2,0]))) # (N,P,3)\n",
"r = expand_NP(torch.cuda.FloatTensor([0.05])) # (N,P,1)\n",
"up = expand_NP(torch.cuda.FloatTensor([0.,1.,0]))# Up vector of camera Useful for cone rays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we obtain $t$ value from ray sphere intersection. But in ORCa pipeline, SDFNet would give us t values. Here consider center $c$ and radius of sphere $R$ are known. In ORCa, center won't be needed and radius can be computed from SDF."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def intersect_ray_sphere(o, d, c, R):\n",
" # Sphere given by <x-c,x-c>=R**2\n",
" # Ray given by x = o+td\n",
" # Point of intersection t follows:\n",
" # a2t^2 + a1t + a0 with following values\n",
" a2 = 1.\n",
" a1 = 2*dot(d, o-c)\n",
" a0 = norm(o-c)**2 - R**2\n",
" # Using quadratic formula finding the smaller of the two roots\n",
" t_int = (-a1 - torch.sqrt(a1**2 - 4*a2*a0))/(2*a2)\n",
"\n",
" return o+t_int*d\n",
"\n",
"\n",
"R = expand_NP(torch.cuda.FloatTensor([1.])) # (N,P,1)\n",
"c = expand_NP(torch.cuda.FloatTensor([0., 0., 0.])) # (N,P,3)\n",
"\n",
"p = intersect_ray_sphere(o,d,c,R)\n",
"# t is distance along ray\n",
"t = norm(p-o) # (N,P,1)\n",
"# surface normal points outwards from center\n",
"n = normalize(p-c) # (N, P, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we visualize the known quantities. As all the $N \\times P$ values are the same we just visualize the first one. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3cec8a9cf19491182a91b364c9cf959",
"version_major": 2,
"version_minor": 0
},
"image/png":
MIT License Copyright (c) 2023 Kushagra Tiwary
12024.229 data compilation (work of non-fiction)
..........
Violent War Criminal Identification, Tracking, and Capture
Post War Crime:
Part I
Quantum Tomography
Satellite Surveillance
Airborne DNA Capture
Drone Video Capture
Smart Device Video Capture
Airborne Data Capture (Project x)
Part II
Deep Learning Neural Network
Crime Scene Reconstruction
Rapid DNA Profiling
Part III
Summary Committee Judgement
Deploy Hunter-Killer Drone(s)/Human(s)
-break-
MIT link
Drone DNA Sniffers link
Data Network Mapping link
Victim DNA Storage and Future Rebirth link
Blade Runner clip:
Blade Runner Enhance Scene Colton Casados-Medve
...........
NeRF: Neural Radiance Fields Matthew Tancik
-access to raw unedited image and video data required for ML training (cloud data)
..........
Shiny object environment reconstruction
https://blogbarley.blogspot.com/2024/08/diagram-to-do.html
Blade Runner 2049: Holographic Girlfriend (HD CLIP)
Personal holographic A/V black box (viewable only to the user) https://blogbarley.blogspot.com/2024/11/personal-holographic-av-black-box.html
https://blogbarley.blogspot.com/2024/02/this-might-get-removed-to-more-secure.html
https://blogbarley.blogspot.com/2025/04/252025114-cybernetic-control-system-and.html
https://blogbarley.blogspot.com/2024/11/direct-data-interface.html
https://news.mit.edu/2023/using-reflections-shiny-objects-camera-0510
https://barleysarthistory.blogspot.com/2024/08/diagram-12024236.html
Projected directly to the eye https://blogbarley.blogspot.com/2024/11/youtube-video-player.html
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis https://arxiv.org/abs/2003.08934
https://barleysarthistory.blogspot.com/2024/08/12024229-data-compilation.html
body motion identification algorithm https://ieeexplore.ieee.org/document/9546980
=======================
Locate the shiny objects in photos/video
Post War Crime:
Part I
Quantum Tomography
Satellite Surveillance
Airborne DNA Capture
Drone Video Capture
Smart Device Video Capture
Airborne Data Capture (Project x)
Part II
Deep Learning Neural Network
Crime Scene Reconstruction
Rapid DNA Profiling
Part III
Summary Committee Judgement
Deploy Hunter-Killer Drone(s)/Human(s)
Nano camera for Project G/Alpha 008 https://blogbarley.blogspot.com/2024/12/httpswww_6.html
Return to Camelot Project https://blogbarley.blogspot.com/2025/01/11980-artorius-initiative.html
Project G
Alpha 008
sci-fi project 004Щф
sci-fi project 004
sci-fi project H00
sci-fi project АБ11
Project Shiva
RACE
#DHS #DARPA #CIA #AFRL
Academic study & data collection by Barley
Eternal Empire Server Property
