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+in+large-scale%252C+dynamic+urban+areas.+Existing+works+are+not+well+suited+for+applications+like+mixed-reality+or+closed-loop+simulation+due+to+their+limited+visual+quality+and+non-interactive+rendering+speeds.+Recently%252C+rasterization-based+approaches+have+achieved+high-quality+NVS+at+impressive+speeds.+However%252C+these+methods+are+limited+to+small-scale%252C+homogeneous+data%252C+i.e.+they+cannot+handle+severe+appearance+and+geometry+variations+due+to+weather%252C+season%252C+and+lighting+and+do+not+scale+to+larger%252C+dynamic+areas+with+thousands+of+images.+We+propose+4DGF%252C+a+neural+scene+representation+that+scales+to+large-scale+dynamic+urban+areas%252C+handles+heterogeneous+input+data%252C+and+substantially+improves+rendering+speeds.+We+use+3D+Gaussians+as+an+efficient+geometry+scaffold+while+relying+on+neural+fields+as+a+compact+and+flexible+appearance+model.+We+integrate+scene+dynamics+via+a+scene+graph+at+global+scale+while+modeling+articulated+motions+on+a+local+level+via+deformations.+This+decomposed+approach+enables+flexible+scene+composition+suitable+for+real-world+applications.+In+experiments%252C+we+surpass+the+state-of-the-art+by+over+3+dB+in+PSNR+and+more+than+200+times+in+rendering+speed.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.19764d71e479a116b53781187cdb8479%3frik%3dndqScqdGHOjeTg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=3D+Gaussian+Vector+Field&FORM=IRPRST&ck=054DA41D7CEB80CE646442F8510A6760&selectedIndex=19&itb=0)
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