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TripoSplatWebGPU / assets /sharpWorker-DY5qaShx.js

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Deploy TripoSplat WebGPU app (production)

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Expected ${c}.`);lt("running-inference","Running SHARP inference in the browser…",t);const f={[i.inputNames[0]]:new he("float32",s,[1,3,Ft,Ft]),[i.inputNames[1]]:new he("float32",new Float32Array([r.disparityFactor]),[1])};u&&(f[i.inputNames[2]]=new he("float32",new Float32Array([r.focalPx]),[1]),f[i.inputNames[3]]=new he("float32",new Float32Array([r.imageWidth]),[1]),f[i.inputNames[4]]=new he("float32",new Float32Array([r.imageHeight]),[1]));const m=await i.run(f),h=ru(m),{data:y,count:g}=Et(h.meanVectors,3,h.isNdcOutput?"mean_vectors_ndc":"mean_vectors"),{data:v,count:T}=Et(h.singularValues,3,h.isNdcOutput?"singular_values_ndc":"singular_values"),{data:x,count:O}=Et(h.quaternions,4,h.isNdcOutput?"quaternions_ndc":"quaternions"),{data:C,count:N}=Et(h.colors,3,"colors"),{data:L,count:M}=Et(h.opacities,1,"opacities");if(g!==T\|\|g!==O\|\|g!==N\|\|g!==M)throw new Error(`Output count mismatch: means=${g}, scales=${T}, quat=${O}, colors=${N}, opacities=${M}`);lt("filtering","Filtering and capping Gaussians for browser preview/export…",t);const{pruned:G,totalCount:U}=Xs(y,v,x,C,L,r.opacityThreshold,r.maxGaussians);if(h.isNdcOutput){lt("filtering","Converting NDC Gaussians to metric space in-browser…",t);const W=r.imageWidth/(2r.focalPx),R=r.imageHeight/(2*r.focalPx);tu(G,W,R)}lt("building-ply","Building binary .ply for preview and download…",t);const D=Vs({...G,imageWidth:r.imageWidth,imageHeight:r.imageHeight,focalPx:r.focalPx}),Z={plyBuffer:D.buffer.slice(D.byteOffset,D.byteOffset+D.byteLength),selectedGaussians:G.count,totalGaussians:U};qt({type:"reply",requestId:t,ok:!0,result:Z},[Z.plyBuffer])}Xr.onmessage=async t=>{const{data:r}=t;try{if(r.type==="load-model"){await nu(r.requestId,r.payload);return}if(r.type==="run-inference"){await au(r.requestId,r.payload);return}throw new Error(`Unknown worker request type: ${r.type??"undefined"}`)}catch(i){qs(r.requestId,i)}}})();