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nx_gsplat_optimize_gate.nx
buildroot/runtime/_hdl_build/nx_gsplat_optimize_gate.nx
about
nx_gsplat_optimize_gate.nx -- ★THE GENERATOR: a sovereign differentiable-splatting COLOUR optimizer fits
Gaussians to a target image by gradient descent (the 3DGS training mechanism, colour channel). Integer,
deterministic. Proves the loss DROPS + the render converges to the target.
T1 a Gaussian sphere with KNOWN colours -> render = the TARGET. Reset all colours to GREY (wrong). Optimize:
the image loss must DROP hard (final < 30% of initial) and MONOTONICALLY (each iter <= prev)
T2 the recovered render MATCHES the target (final loss small = the colours were fit back)
T3 determinism (same init -> same optimized colours) + PNG knowledge/nx_gsplat_opt.png (target | optimized)
license_tier: ORIGINAL expect_exit: 0
dependencies 4 imports · 0 importers
imports: nx_syscalls.nxnx_itrig.nxnx_png.nxnx_gsplat.nx
imported by: nobody (leaf or entry point)
call flow from main pre-order; caps 40 nodes / depth 6 declared; ↻ = already shown
structs
| none |
consts
| none |
functions
| 14 | func hw(s: *u8) -> i64 { var n: i64=0; while s[n]!=(0 as u8){n=n+1} sys_write(1,s,n); return 0 } |
| 15 | func pn(v: i64) -> i64 { let b: *u8=sys_mmap(32) as *u8; var x: i64=v; var ng: i64=0; if x<0{ng=1;x=0-x} var i: i64=31; if x==0{b[i]=48 as u8;i=i-1} while x>0{b[i]=(48+x%10) as u8;x=x/10;i=i-1} if ng==1{b[i]=45 as u8;i=i-1} sys_write(1,(b as i64+i+1) as *u8,31-i); return 0 } |
| 17 | func build_sphere(gauss: *i64) -> i64 { // 768 gaussians, spatially-varying TRUE colours |
| 44 | func main() -> i64 |