๐Ÿ“š Study 97

NeRF ๊ฐ„๋‹จ ์„ค๋ช… with ์•ฝ๊ฐ„์˜ ์ฝ”๋“œ

[Paper Review] NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV2020)NeRF ๋ชจ๋ธ์€ ๋งŽ์€ ๋ธ”๋กœ๊ทธ์™€ ์œ ํŠœ๋ธŒ ์ž๋ฃŒ๋ฅผ ์ฐพ์•„๋ณด๋ฉฐ ์ดํ•ดํ•˜๋Š” ์ˆ˜์ค€์— ๊ทธ์ณค๋Š”๋ฐ ๋…ผ๋ฌธ์„ ์ •๋…ํ•˜๋‹ˆ ํ›จ์”ฌ ๋” ์ดํ•ด ์ •๋„๊ฐ€ ๊นŠ์–ด์ง„ ๊ธฐ๋ถ„์ด๋‹ค. ์ง์ ‘ ๊ธ€์„ ์จ๋ณด๋ฉฐ ์™„๋ฒฝํžˆ ๋‚ด ๊ฒƒ์œผ๋กœ ๋งŒ๋“ค์ž! ๋‹ค์Œ๊ณผ ๊ฐ™์€ dusruddl2.tistory.com ↑ ๋…ผ๋ฌธ ๋ฆฌ๋ทฐ๋ฅผ ํ–ˆ์—ˆ๋Š”๋ฐ ์ •๋ง ๋‚ด๊ฐ€ NeRF ๋ชจ๋ธ์„ ์ œ๋Œ€๋กœ ์ดํ•ดํ•˜๊ณ  ์žˆ๋‚˜? ์˜๋ฌธ์ด ๋“ค์–ด์„œ ์“ฐ๊ฒŒ ๋œ ํฌ์ŠคํŠธ ํ—ท๊ฐˆ๋ ธ๋˜ ๋ถ€๋ถ„ ์œ„์ฃผ๋กœ ๊ฐ„๋‹จ ๋ฆฌ๋ทฐํ•  ์˜ˆ์ •์ด๋‹ค. nerf/tiny_nerf.ipynb at master · bmild/nerfCode release for NeRF (Neural Radiance Fields..

[Paper Review] 3D Gaussian Splatting for Real-Time Radiance Field Rendering (SIGGRAPH 2023)

3DGS๋ฅผ ์ฒ˜์Œ ๊ณต๋ถ€ํ•˜์‹œ๋Š” ๋ถ„๋“ค์ด๋ผ๋ฉด xoft๋‹˜์˜ ๋ธ”๋กœ๊ทธ์™€ ์œ ํŠœ๋ธŒ ๊ฐ•์˜๋ฅผ ๋จผ์ € ๋“ค์œผ์‹œ๋Š”๊ฑธ ์ถ”์ฒœ๋“œ๋ฆฝ๋‹ˆ๋‹ค.์ „์ฒด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ดํ•ดํ•˜๊ธฐ ์‰ฝ๊ฒŒ ๋‹ค๋ค„์ฃผ์‹œ๊ธฐ ๋•Œ๋ฌธ์— ์ดํ•ด๊ฐ€ ์‰ฝ์Šต๋‹ˆ๋‹ค :) ๋ณธ ๊ธ€์€ ๋…ผ๋ฌธ์„ ์ˆœ์„œ๋Œ€๋กœ ์ฝ๊ณ  ์‹ถ์€ ๋ถ„์—๊ฒŒ ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•ฉ๋‹ˆ๋‹ค. (xoft๋‹˜์˜ ๊ธ€์„ ๋งŽ์ด ์ฐธ๊ณ ํ•˜์˜€์Šต๋‹ˆ๋‹ค.)๋ถ€์กฑํ•œ ์ง€์‹์œผ๋กœ ์ž‘์„ฑํ•œ ๊ธ€์ด๊ธฐ ๋•Œ๋ฌธ์— ์ž˜๋ชป๋œ ๋ถ€๋ถ„์ด ์žˆ์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋งˆ์Œ๊ป ์ง€์ ํ•ด์ฃผ์„ธ์š”! 1. IntroductionMLP๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋Š” NeRF ๊ธฐ๋ฐ˜์˜ ๋ชจ๋ธ๋“ค์€ ๋ Œ๋”๋ง ์†๋„๊ฐ€ ๋„ˆ๋ฌด ๋А๋ ค ์‹ค์ œ ์‘์šฉ์—๋Š” ์ œํ•œ์ ์ด์—ˆ๋Š”๋ฐ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” 3DGS๋ฅผ ํ†ตํ•ด(1) training ์‹œ๊ฐ„๋„ ์ด์ „ ๋ฐฉ๋ฒ•์ฒ˜๋Ÿผ ๋น ๋ฅด๊ฒŒ ๊ทธ๋ฆฌ๊ณ  (2) ํ€„๋ฆฌํ‹ฐ๋„ ์œ ์ง€ํ•˜๋ฉด์„œ (3) ๋ Œ๋”๋ง ์†๋„๋ฅผ ๋งค์šฐ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค.(real-time, high-qual..

3DGS์˜ tile rasterizer์—์„œ ๊ฒน์น˜๋Š” tile๊ฐœ์ˆ˜์— ๋”ฐ๋ผ ์ธ์Šคํ„ด์Šคํ™”ํ•˜๋Š” ์ด์œ ?

3DGS์˜ tile rasterizer ๋ถ€๋ถ„์„ ์ฝ๋‹ค๊ฐ€ ์™œ ๊ทธ๋ ‡์ง€?ํ•˜๋Š” ์ƒ๊ฐ์„ ๋“ค๊ฒŒ ํ•œ ๋ถ€๋ถ„์ด ์žˆ์—ˆ๋‹ค. We then instantiate each Gaussian according to the number of tiles they overlap and assign each instance a key that combines view space depth and tile ID. ์™œ ๊ฐ€์šฐ์‹œ์•ˆ์„ ๊ทธ๋“ค์ด ๊ฒน์น˜๋Š” ํƒ€์ผ ์ˆ˜๋กœ ์ธ์Šคํ„ด์Šคํ™”ํ•˜๋Š” ๊ฒƒ์ผ๊นŒ? ์ด๋Š” ํ•ด๋‹น ๊ฐ€์šฐ์‹œ์•ˆ์ด ์ด๋ฏธ์ง€์— ์–ด๋А์ •๋„ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋ผ๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ํ•œ ์ด๋ฏธ์ง€๊ฐ€ ๋…ผ๋ฌธ์ฒ˜๋Ÿผ 16x16 ํƒ€์ผ๋กœ ๋ถ„ํ• ๋˜์–ด ์žˆ๋‹ค๊ณ  ์ƒ๊ฐํ•ด๋ณด์ž. ์ด๋ฏธ์ง€์— projection๋œ 2D ๊ฐ€์šฐ์‹œ์•ˆ๋“ค์„ ๊ณ ๋ คํ•ด๋ณผ ๋•Œ, ๊ฐ€์šฐ์‹œ์•ˆ์ด ๊ฒน์น˜๋Š” ํƒ€์ผ ์ˆ˜๊ฐ€ ๋งŽ์„ ๊ฒฝ์šฐ -..

3DGS์—์„œ ๋ทฐ ์ ˆ๋‘์ฒด view frustum์ด๋ž€?

3DGS์—์„œ Cull 3D Gaussian์„ ํ•  ๋•Œ,view frustum๊ณผ์˜ ๊ต์ฐจ๊ฐ€ 99%์ธ ๊ฐ€์šฐ์‹œ์•ˆ๋งŒ์„ ๋‚จ๊ธฐ๊ณ  ๋‚˜๋จธ์ง€๋Š” ์ œ๊ฑฐํ•œ๋‹ค๊ณ  ํ–ˆ๋‹ค.6 FAST DIFFERENTIABLE RASTERIZER FOR GAUSSIANSwe only keep Gaussians with a 99% confidence interval intersecting the view frustum ๋„๋Œ€์ฒด ๋ทฐ ์ ˆ๋‘์ฒด๊ฐ€ ๋ฌด์—‡์ธ์ง€ ์ดํ•ดํ•ด๋ณด๋„๋ก ํ•˜์ž ๋ทฐ ์ ˆ๋‘์ฒด(view frustum)์ด๋ž€?ํ”ผ๋ผ๋ฏธ๋“œ ๊ฐ™์€ ๋ชจ์–‘์˜ ์œ—๋ถ€๋ถ„์„ ๋ฐ‘๋ฉด์— ๋ณ‘๋ ฌ๋กœ ์ž˜๋ผ๋‚ธ ์ž…์ฒด ํ˜•์ƒ์ฆ‰, ์นด๋ฉ”๋ผ๊ฐ€ ๋ณผ ์ˆ˜ ์žˆ๋Š” ์˜์—ญ์ด๋ผ๊ณ  ์ƒ๊ฐํ•˜๋ฉด ์ข‹๋‹ค.  ์นด๋ฉ”๋ผ์™€ ๋ง‰๋Œ€๊ธฐ๋ฅผ ์„ค๋ช…ํ•œ ์˜ˆ์‹œ์ธ ์•„๋ž˜์˜ 3๊ฐ€์ง€ ๊ฒฝ์šฐ๋ฅผ ์ƒ๊ฐํ•ด๋ณด์ž.1) ์นด๋ฉ”๋ผ ๋ฐ”๋กœ ์•ž์— ๋ง‰๋Œ€๊ธฐ๊ฐ€ ์žˆ๋‹ค๋ฉด -> ์นด๋ฉ”๋ผ์—๋Š” ์ ๋งŒ์ด ๋ณด์ผ ๊ฒƒ์ด๋‹ค..

3DGS์—์„œ ์•ŒํŒŒ ๋ธ”๋ Œ๋”ฉ α-blending ์ด๋ž€?

3DGS ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” α-blending๋„๋Œ€์ฒด ์ด ๋…ผ๋ฌธ์—์„œ ์˜๋ฏธํ•˜๋Š” ์•ŒํŒŒ ๋ธ”๋ Œ๋”ฉ์ด ๋ฌด์—‡์ธ์ง€ ๋‚ด ์ƒ๊ฐ์„ ์ •๋ฆฌํ•ด๋ณด๊ฒ ๋‹ค. ์•ŒํŒŒ ๋ธ”๋ Œ๋”ฉ์ด๋ž€?์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋“ค์„ ํ•ฉ์„ฑํ•  ๋•Œ, ํˆฌ๋ช…๋„(α)๊ฐ’์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ ํ˜ผํ•ฉํ•˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค.์•„๋ž˜ ์‚ฌ์ง„๊ณผ ๊ฐ™์ด α๊ฐ’์ด ์ž‘์œผ๋ฉด ๋” ํˆฌ๋ช…ํ•ด์ง€๊ณ , α๊ฐ’์ด ์ปค์ง€๋ฉด ๋” ๋ถˆํˆฌ๋ช…ํ•ด์ง„๋‹ค. ๊ธฐ์กด์—๋Š” ์ด๋ฏธ์ง€๋ฅผ ์˜ค์ง RGB 3๊ฐ€์ง€๋กœ ํ‘œํ˜„์„ ํ–ˆ๋‹ค๋ฉด,์ด๋ฏธ์ง€์˜ ํˆฌ๋ช…ํ•œ ํšจ๊ณผ๋ฅผ ๊ตฌํ˜„ํ•˜๊ฑฐ๋‚˜ (ex. ๋กœ๊ณ  ์ด๋ฏธ์ง€์˜ ๋ฐฐ๊ฒฝ ํˆฌ๋ช…ํ•˜๊ฒŒ or ๊ทธ๋ฆผ์ž ํšจ๊ณผ)์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ•ฉ์„ฑํ•  ๋•Œ ๊ฐ ํ”ฝ์…€์˜ ๊ฐ€์ค‘์น˜๋ฅผ ์ œ์–ดํ•  ๋•Œ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ดRGB์™ธ์— ํˆฌ๋ช…๋„๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ RGBA 4๊ฐ€์ง€๋กœ ํ‘œํ˜„์„ ํ•œ๋‹ค.  ์ด ๊ธฐ์ˆ ์„ 3DGS ๋ชจ๋ธ์—๋Š” ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉํ•œ๋‹ค๋Š”๊ฑธ๊นŒ? 3DGS ๋ชจ๋ธ์€ SFM points๋ฅผ ํ†ตํ•ด 3D Gaussian์„ ๋งŒ๋“  ํ›„,proj..

[Paper Review] NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV2020)

NeRF ๋ชจ๋ธ์€ ๋งŽ์€ ๋ธ”๋กœ๊ทธ์™€ ์œ ํŠœ๋ธŒ ์ž๋ฃŒ๋ฅผ ์ฐพ์•„๋ณด๋ฉฐ ์ดํ•ดํ•˜๋Š” ์ˆ˜์ค€์— ๊ทธ์ณค๋Š”๋ฐ ๋…ผ๋ฌธ์„ ์ •๋…ํ•˜๋‹ˆ ํ›จ์”ฌ ๋” ์ดํ•ด ์ •๋„๊ฐ€ ๊นŠ์–ด์ง„ ๊ธฐ๋ถ„์ด๋‹ค. ์ง์ ‘ ๊ธ€์„ ์จ๋ณด๋ฉฐ ์™„๋ฒฝํžˆ ๋‚ด ๊ฒƒ์œผ๋กœ ๋งŒ๋“ค์ž! ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ˆœ์„œ๋กœ ๊ธ€์ด ์ง„ํ–‰๋œ๋‹ค. 0. [Abstract] NeRF ๊ฐ„๋‹จ ์„ค๋ช… 1. [Background] Explicit Representation vs Implicit Representation 2. Neural Radiance Field Scene Representation 2.1 Overview 2.2 MLP Network 3. Volume Rendering with Radiance Field 3.1 [Equation 1] The expected color of the input ray. 3.2 [ Equation 2]..

[Git] ๋กœ์ปฌ์— ์žˆ๋Š” ํด๋”๋ฅผ ๊นƒํ—ˆ๋ธŒ Repo์— ์˜ฌ๋ฆฌ๋Š” ๋ฐฉ๋ฒ•

๋กœ์ปฌ์— ์ €์žฅํ•ด๋‘” workspace ํด๋”๋ฅผ ๊นƒํ—ˆ๋ธŒ๋กœ ์˜ฌ๋ฆด ์˜ˆ์ •์ด๋‹ค. ๋‹ค์Œ ๋‘ ๊ฐ€์ง€๋Š” ์ด๋ฏธ ์ค€๋น„๋˜์–ด ์žˆ๋‹ค๋Š” ๊ฐ€์ •ํ•˜์— ์ง„ํ–‰ํ•˜๊ฒ ๋‹ค 1. Git Bash ์„ค์น˜ 2. Github Repo ์ƒ์„ฑ ํ›„ copy๋œ ์ฃผ์†Œ ๋‹ค์Œ ๊ณผ์ •์„ ํ•œ ๋‹จ๊ณ„์”ฉ ์ฐจ๊ทผ์ฐจ๊ทผ ์ˆ˜ํ–‰ํ•˜๋ฉด ๋œ๋‹ค. $ git init $ git status $ git add . $ git commit -m "commit message" $ git remote add origin ***(copied ์ฃผ์†Œ) $ git remote -v $ git push origin master $ git init ๋กœ์ปฌ ์ €์žฅ์†Œ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋‹จ๊ณ„๋กœ, ์‹คํ–‰ ์ดํ›„, workspace ํด๋” ์•ˆ์— .git์ด๋ผ๋Š” ํด๋”๊ฐ€ ์ƒ๊ธด๋‹ค *๊ฐ€๋” ์˜ค๋ฅ˜๊ฐ€ ์ƒ๊ธฐ๋Š” ๊ฒฝ์šฐ์—๋Š”, .git ํด๋” ์‚ญ์ œ ํ›„ ๋‹ค์‹œ git ini..

[Linux] ๋ช…๋ น์–ด du - ํด๋”๋ณ„ ์šฉ๋Ÿ‰ ํ™•์ธํ•˜๊ธฐ

vscode์—์„œ ํŒŒ์ผ์˜ ์šฉ๋Ÿ‰์„ ํ™•์ธํ•˜๊ณ  ์‹ถ์„ ๋•Œ๋Š” ๋‹ค์Œ ํ™•์žฅํ”„๋กœ๊ทธ๋žจ์„ ์„ค์น˜ํ•ด์„œ ์‚ฌ์šฉํ•˜๋ฉด ๋˜์—ˆ๋‹ค ์˜ค๋ฅธ์ชฝ ๋งˆ์šฐ์Šค๋ฅผ ๋ˆ„๋ฅด๋ฉด ์œ„์™€ ๊ฐ™์ด View File Properties๊ฐ€ ์ƒ๊ฒจ์„œ ํŒŒ์ผ ์šฉ๋Ÿ‰์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ํ”„๋กœ๊ทธ๋žจ์˜ ๊ฒฝ์šฐ ํด๋”์˜ ์šฉ๋Ÿ‰์€ ํ™•์ธํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ์—ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹ค์Œ ๋ช…๋ น์–ด๋ฅผ ํ†ตํ•ด ์ด๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค du -sh ./ # ํ˜„์žฌ ํด๋”์˜ ์ด ์‚ฌ์šฉ ์šฉ๋Ÿ‰ du -sh */ # ํ˜„์žฌ ํด๋”์˜ ํ•˜์œ„ ํด๋”๋ณ„ ์šฉ๋Ÿ‰ du -sh ./* # ํ˜„์žฌ ํด๋”์˜ ๋ชจ๋“  ํŒŒ์ผ, ํด๋” ์šฉ๋Ÿ‰ ๋‹ค์Œ ํด๋”๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๊ฐ ๋ช…๋ น์–ด๋ฅผ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ๋Š” ์•„๋ž˜์™€ ๊ฐ™๋‹ค. ์ˆœ์„œ๋Œ€๋กœ - ์ด ์‚ฌ์šฉ ์šฉ๋Ÿ‰ - ํ•˜์œ„ ํด๋”๋ณ„ ์šฉ๋Ÿ‰ - ๋ชจ๋“  ํŒŒ์ผ, ํด๋” ์šฉ๋Ÿ‰ ์ž˜ ์ถœ๋ ฅํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.

[Vscode] Remote-SSH ์„œ๋ฒ„ ์—ฐ๊ฒฐํ•˜๋Š” ๋ฒ•

์ฒ˜์Œ์œผ๋กœ ํ•ด๋ณด๋Š” ์ž‘์—…์ด๋ผ ๋ช‡์‹œ๊ฐ„์ด ๊ฑธ๋ ค ๊ฒจ์šฐ ํ•ด๊ฒฐํ–ˆ๋˜ ๊ณผ๊ฑฐ์˜ ๋‚˜.. ์ตœ๊ทผ ๋‹ค๋ฅธ ์„œ๋ฒ„๋กœ ์—ฐ๊ฒฐํ–ˆ๋Š”๋ฐ ๋ช‡๋ถ„๋„ ์ฑ„ ๊ฑธ๋ฆฌ์ง€ ์•Š์•„ ๊ธ€์„ ์”๋‹ˆ๋‹ค. STEP1) extensions์—์„œ Remote-SSH ๊น”๊ธฐ ์ฒซ๋ฒˆ์งธ๊ฑฐ๋งŒ ๊น”๋ฉด ๋‚˜๋จธ์ง€๋Š” ์ž๋™์œผ๋กœ ๊น”๋ ธ๋˜ ๊ฒƒ ๊ฐ™๋„ค์š”! STEP2) config ํŒŒ์ผ ๋„ฃ๊ธฐ ctrl+shift+p์„ ๋ˆŒ๋Ÿฌ์„œ ๋‹ค์Œ์œผ๋กœ ๋“ค์–ด๊ฐ€๊ธฐ (.ssh\config ๊ฒฝ๋กœ๋กœ ๋“ค์–ด๊ฐ€์ฃผ์„ธ์š”) ์ฐธ๊ณ ๋กœ ์ •๋ง ๋‹ค์–‘ํ•œ ๋ธ”๋กœ๊ทธ๋ฅผ ๋ณด๋ฉด์„œ config ํŒŒ์ผ์„ ๋งŒ๋“ค์—ˆ์—ˆ๋Š”๋ฐ ๊ณ„์† ์—๋Ÿฌ๊ฐ€ ๋– ์„œ ๊ทธ๊ฒƒ๋ณด๋‹ค ์ €์˜ ๊ฒฝ์šฐ์—๋Š” ๊ทธ๋ƒฅ ์„œ๋ฒ„ ์ฃผ์†Œ๋กœ jupyter notebook์— ๋“ค์–ด๊ฐ€ ssh์— ๊ด€ํ•œ ํŒŒ์ผ์„ ๋‹ค์šด ๋ฐ›๋Š”๊ฒŒ ์˜ค๋ฅ˜ ์—†์ด ์ข‹์•˜์Šต๋‹ˆ๋‹ค.. ํŒŒ์ผ์„ ๋‹ค์šด ๋ฐ›์•„ vscode๋กœ ์—ด๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™์€๋ฐ์š” ์„œ๋ฒ„ ์ด๋ฆ„๋งŒ ์ง€์ •ํ•ด์„œ $SSH_CONNECTION_NA..

[Vscode] venv ๊ฐ€์ƒ ํ™˜๊ฒฝ๊ณผ ipynb ํŒŒ์ผ์˜ kernel ์—ฐ๊ฒฐํ•˜๋Š” ๋ฒ• (python environment)

Select kernel์„ ๋ˆŒ๋ €์„ ๋•Œ ์–ด๋–ค ๊ฒฝ์šฐ์—๋Š” ๋‚ด๊ฐ€ ์›ํ•˜๋Š” venv ๊ฐ€์ƒํ™˜๊ฒฝ์ด ์กด์žฌํ•˜์ง€๋งŒ ๋˜ ๋‹ค๋ฅธ ๊ฒฝ์šฐ์—๋Š” ์—†์„ ๋•Œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด๋ฒˆ ๊ธฐํšŒ์— ์ œ๋Œ€๋กœ ์•Œ์•„๋‘๊ธฐ๋กœ ํ•˜์ž! Select Kernel์„ ๋ˆ„๋ฅด๋ฉด Python Environment์™€ Jupyter๋กœ ์—ฐ๊ฒฐํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋œจ๋Š”๋ฐ ๋ณธ ๊ฒŒ์‹œ๋ฌผ์—์„œ๋Š” Python Environment ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃฐ ์˜ˆ์ •์ด๋‹ค. ์‚ฌ์šฉํ•  ๊ฐ€์ƒํ™˜๊ฒฝ์€ ๋ฏธ๋ฆฌ ๋งŒ๋“ค์–ด๋‘” test_env์ด๋‹ค. STEP1) Ctrl + Shift + P๋ฅผ ๋ˆŒ๋Ÿฌ 'Select Interpreter' ๋กœ ๋“ค์–ด๊ฐ€๊ธฐ STEP2) Enter interpreter path ๋ˆ„๋ฅด๊ธฐ ์œ„ ์‚ฌ์ง„์˜ ๊ฒฝ์šฐ์—๋Š” ๋ฏธ๋ฆฌ ์ด ๊ณผ์ •์„ ๊ฑฐ์ณค๊ธฐ ๋•Œ๋ฌธ์— test_env๊ฐ€ ์žˆ์ง€๋งŒ ์ฒ˜์Œ ํ•ด๋ณด๋Š”๊ฑฐ๋ผ๋ฉด ์›ํ•˜๋Š” ๊ฐ€์ƒํ™˜๊ฒฝ์ด ์—†์„ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  Find๋ฅผ ๋ˆŒ..