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command: python C:/Users/Thominator/Desktop/InstantNGP/scripts/colmap2nerf.py video_in video.MOV video_fps 2 run_colmap aabb_scale 16 patreon 🤍 my website 🤍 #nerf #instantngp
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NeRF, ADOP, Plenoxel, now instant NGP?! we are at the peak of NeRF research. I did not expect finding a better solution for neural radiance fields would be such a trending research topic, and it's just getting started. This field of AI is going to be incredible. As of the time of publishing, another eye catching NeRF research just came out, I can't keep up with these. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [Official Project Page] 🤍 [Official Paper] 🤍 [Official GitHub] 🤍 [Installation Tutorial] 🤍 [Installation GitHub] 🤍 This video is supported by the kind Patrons & YouTube Members: 🙏Andrew Lescelius, Panther Modern, Sascha Henrichs, Jake Disco, Demilson Quintao, Tony Jimenez, dicefist, Shuhong Chen, Hongbo Men, happi nyuu nyaa, Carol Lo, PyroBot [Website] 🤍 [Discord] 🤍 [Twitter] 🤍 [Patreon] 🤍 [PayPal] 🤍 [Music] Zeeky - Springfield [Profile & Banner Art] 🤍
Neighbouring Group Participation (NGP) is observed in nucleophilic substitution reactions, where a neighbouring group helps in the removal of the leaving group to form a reactive intermediate that leads to the formation of the product. Increase in the reaction rate and unexpected stereo chemical outcomes are associated in reactions involving NGP. An atom having an unshared pair of electrons and also present at least beta to the leaving group can act as a neighbouring group. Also, NGP is mostly observed on solvolysis reactions where the solvent acts as the nucleophile. During NGP, the neighbouring group (G) attacks the electrophilic centre to eliminate the leaving group (L). This leads to the formation of a cyclic intermediate which is very reactive. This is called anchimeric assistance from the neighbouring group. The nucleophile (Nu-)then attacks this intermediate to form the product. If the attack happens of the carbon that was having the leaving group the configuration will be retained because the configuration at that carbon will be inverted twice. Groups like halides, hydroxides, ethers, thio ethers, amino groups, carboxylates, phenyl group, pi-bonds etc. have been indentified to act as neighbouring groups in many reactions. The mechanism for anchimeric assistance is a two step mechanism where two consecutive SN2 reaction leads to retention of configuration. In the first step, the neighbouring group (Z) acts as a nucleophile, attacking the substitution centre and expelling out the leaving group. In the next step, the external nucleophile (Y) attack from backside displacing the neighbouring group and retaining the overall configuration. Since the first step is slow and is rate determining, the reaction follows first order kinetics and there is no effect of concentration of Y- on rate of reaction. Anchimeric assistance enhances rate of reactions by several order of magnitudes. This is because step I is the rate determining step and the neighbouring group Z which is readily available within the substrate makes the attack much faster as compared to attack by any external nucleophile Y for which, to react, the substrate has to collide with Y. Since, Z is readily available by virtue of its position its attack is much faster. Thermodynamically also, anchimeric assistance is favoured as the reaction between the substrate and Y- involves a large decrease in entropy of activation (∆S†), as the reactants are far less free in the transition state than before. Reaction of Z involves a much smaller loss of entropy. Important atoms and groups that can act as neighbouring groups are COO, COOR, COAr, OCOR, OR, OH, O, NH2, NHR, NR2, NHCOR, SH, SR, S, SO2Ph, I, Br, and Cl. The effectiveness of halogens as neighbouring groups decreases in the order I ,,Br, Cl. The chloride is a very weak neighbouring group and can be shown to act in this way only when the solvent does not interfere. Thanks Team IITian explains
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#ngpnews #hindinews #nagpur कामठी स्तिथ वारिसपूरा परिसर में महिला की सदिग्ध मौत से इलाके में मची सनसनी
NOTE: there's a faster way to extract the images, shown at 1:20 - read these notes! Video made is here: 🤍 Watch it loop here: 🤍 The model was filmed at Chesterwood, in Stockbridge, MA, USA. A great place to visit! 🤍 The final NeRF created from this model is here: 🤍 Instant NGP software and build instructions: 🤍 Instructions on making your own NeRFs - extremely useful: 🤍 Locking the exposure when you make a video can definitely help, e.g., 🤍 - short version for iPhone: before recording, hold your finger on the focus point for a few seconds, then press the yellow "AE/AF lock" button that appears above. Problems? For help, see the Discussion area on the site 🤍 and search on all issues at 🤍 Complementary blog post and video with more (somewhat dated) info: 🤍 Need more help? Try the NeRF Discord server: 🤍 or perhaps the reddit group 🤍 (there is also 🤍 but that's for making 3D models, e.g., meshes, from NeRFs). The commands I used in the conda command window ("ngp" is an environment I earlier created for instant-ngp; see it at the bottom of these notes): # Set environment in Anaconda conda activate ngp # Pull images from movie; I've put movie directory "chesterwood" in the instant-ngp directory for simplicity. Change "fps 2" to whatever is needed to give you around 100 images. cd C:\Users\(your path here)\Github\instant-ngp cd chesterwood python ..\scripts\colmap2nerf.py video_in IMG_9471.MOV video_fps 2 run_colmap overwrite # NOTE! This line is a bit different than shown in the video, as advice on aabb_scale's use has changed. Also, I usually want to delete a few images after extracting them, so I don't do an exhaustive match at this point. In fact, I usually hit break (Control-C) when I see "Feature extraction" starting, as the images have all been extracted at that point. #After you delete any blurry or useless frames, continue below to match cameras. # Camera match given set of images. Do for any set of images. Run from directory containing your "images" directory. python C:\Users\(your path here)\Github\instant-ngp\scripts\colmap2nerf.py colmap_matcher exhaustive run_colmap aabb_scale 16 overwrite # For videos or closely related sets of shots, you can take out the "colmap_matcher exhaustive" from the line above, since your images are in order. This saves a few minutes. You could also leave off "aabb_scale 16" or put 64, the new default; the docs say it is worth playing with this number, see nerf_dataset_tips.md for how (short version: edit it in transforms.json). In my limited testing, I personally have not seen a difference. # run interactive instant-ngp - run from the main directory "instant-ngp" cd .. instant-ngp chesterwood (NOTE! If you don't seen the "Camera path" menu, drag the "instant-ngp" menu off of it - the two menus start in the same space, oddly enough. And, if you don't see the "Render video" button in this menu, resize the menu to view it. The menu starts very small. You can also close the "Path manipulation" submenu - click on the arrow to its left - to see the other Camera path options.) The anaconda environment setup commands I personally use to install the packages needed (just need to do these once for the project, other than the "activate"): conda create -n ngp python=3.10 conda activate ngp cd C:\Users\ehaines\Documents\_documents\Github\instant-ngp pip install -r requirements.txt The above creates a conda "environment" called ngp, makes it the active one, then installs the software you need. After creating an environment is created, you can open a new Anaconda window and "conda activate ngp" from anywhere to use it. If you get stuck, you might want to check my previous video's notes: 🤍
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In this video, i've briefly discussed the important examples of NGP. Also the previous year questions are important and have many questions on this concept. More videos coming up! Subscribe for MORE! MORE FREE PRACTICE ON UNACADEMY: 🤍 TELEGRAM GROUPS 🤍 🤍 ORGANIC 'CLAYDEN' CHAPTER DISCUSSION: 🤍 PRACTICE BOOSTER SERIES: 🤍 #substitutionreaction #csirnetexam #chemicalsciences
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This video shows preliminary results of registering 360 photos to create Nerfs using Instant Ngp. #nvidia #instantnerf #360video #360vr #photogrammetry #computervision #3dreconstruction
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We headed to Guangzhou to try out the latest version of XPeng's self-driving tech, City NGP. Follow us! IG: 🤍wheelsboy_auto FB: 🤍 Website: 🤍
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En este video se demuestra la manera correcta de conectar tu controlador a la valvula de llenado de tu termo solar NGP. No olvides visitarnos en nuestra pagina web: 🤍ngp.cl Suscribete y recibiras noticias de nuevos tutoriales.
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This video is all you need to convert a video or image to NeRF scenes on Ubuntu 22.04 machine with Cuda-nn, Python 3.9, colmap, ffmpeg and other libraries.. The new NVIDIA NGP Instant NeRF is a great introduction to getting started with neural radiance fields. With your own video and/or image dataset, you can compile the codebase, prepare your images, and train your first NeRF. Unlike other NeRF implementations, Instant NeRF only takes a few minutes to train a great-looking visual. GitHub Resources: 🤍 Research Paper and Code: 🤍 ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ - (00:00) Quick Demo - (00:45) NeRF intro - (01:45) Content details - (03:35) Setting Ubuntu 22.04 Environment - (08:30) Building instant-ngp library - (10:00) Convert video to NeRF Data - (18:05) NeRF Scene Demo starts - (21:18) NeRF Rendering Options - (24:28) Debug Visualization - (26:35) Saving NeRF Snapshot - (27:26) NeRF Histogram - (28:20) NeRF Volume - (29:32) NeRF Gigapixel Images - (31:52) Conclusion Connect - Prodramp LLC (🤍prodramp) - Website - 🤍 - LinkedIn - 🤍 - GitHub- 🤍 - AngelList - 🤍 - Facebook - 🤍 Content Creator: Avkash Chauhan (🤍avkashchauhan) - 🤍 - 🤍 Tags: #nvidia #ai #nerf #cnn #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #xai #instant-ngp #cuda #cuda-nn
65 images less than 5 minutes to train on an RTX 3090
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