Closed garrisonz closed 8 months ago
Hi, the variance of the 2D Gaussian filter is choose to approximate a box filter with a pixel size. We choose the value of 0.1. Here is a figure showing a box filter with 1 pixel wide and three Gaussian filters with different variances: 0.1, 0.3, 1.
In case anyone's interested, here are benchmarks with kernel size = 0.1 vs 0.3
0.1 is a clear winner on synthetic, but much less on real data
benchmark_nerf_synthetic_ours_mtmt: Multi-scale Training and Multi-scale Testing on the Mip-NeRF 360 dataset
PSNR: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 37.565 | 27.765 | 34.745 | 39.169 | 35.230 | 31.988 | 37.678 | 32.719 | 34.607 | |
ks03 | 35.324 | 26.999 | 32.910 | 37.729 | 32.716 | 30.527 | 35.553 | 31.368 | 32.891 |
SSIM: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 0.991 | 0.963 | 0.990 | 0.991 | 0.988 | 0.979 | 0.994 | 0.933 | 0.979 | |
ks03 | 0.986 | 0.958 | 0.986 | 0.988 | 0.980 | 0.975 | 0.991 | 0.928 | 0.974 |
LPIPS: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 0.009 | 0.031 | 0.009 | 0.010 | 0.011 | 0.018 | 0.005 | 0.059 | 0.019 | |
ks03 | 0.017 | 0.040 | 0.015 | 0.015 | 0.023 | 0.023 | 0.009 | 0.069 | 0.026 |
Count: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 225042 | 346217 | 201422 | 162532 | 277335 | 269540 | 376341 | 428152 | 285822 | |
ks03 | 142865 | 169266 | 94122 | 108496 | 168791 | 157234 | 145845 | 234104 | 152590 |
benchmark_nerf_synthetic_ours_stmt: Single-scale Training and Multi-scale Testing on the Mip-NeRF 360 dataset
PSNR: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 35.615 | 26.463 | 32.998 | 36.141 | 32.853 | 30.112 | 31.713 | 29.704 | 31.950 | |
ks03 | 32.281 | 25.004 | 31.185 | 32.932 | 29.874 | 27.744 | 27.889 | 27.251 | 29.270 |
SSIM: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 0.988 | 0.958 | 0.988 | 0.987 | 0.983 | 0.975 | 0.986 | 0.922 | 0.973 | |
ks03 | 0.976 | 0.943 | 0.982 | 0.977 | 0.966 | 0.962 | 0.965 | 0.905 | 0.959 |
LPIPS: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 0.013 | 0.035 | 0.012 | 0.013 | 0.016 | 0.019 | 0.015 | 0.068 | 0.024 | |
ks03 | 0.025 | 0.050 | 0.019 | 0.019 | 0.033 | 0.025 | 0.023 | 0.081 | 0.034 |
Count: | chair | drums | ficus | hotdog | lego | materials | mic | ship | Average | |
---|---|---|---|---|---|---|---|---|---|---|
orig | 267065 | 342581 | 187353 | 193614 | 295019 | 240818 | 409766 | 442234 | 297306 | |
ks03 | 215105 | 271186 | 166316 | 184540 | 240591 | 191612 | 340624 | 398384 | 251044 |
benchmark_360v2_ours: Multi-scale Training and Multi-scale Testing on the the Blender dataset
PSNR: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 25.904 | 22.062 | 27.973 | 27.141 | 22.689 | 31.890 | 29.288 | 31.770 | 32.572 | 27.921 | |
ks03 | 25.867 | 22.038 | 27.883 | 27.190 | 22.628 | 31.879 | 29.357 | 31.761 | 32.370 | 27.886 |
SSIM: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 0.804 | 0.656 | 0.884 | 0.802 | 0.655 | 0.933 | 0.920 | 0.936 | 0.952 | 0.838 | |
ks03 | 0.803 | 0.653 | 0.882 | 0.802 | 0.658 | 0.933 | 0.921 | 0.936 | 0.951 | 0.838 |
LPIPS: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 0.161 | 0.267 | 0.090 | 0.181 | 0.269 | 0.175 | 0.166 | 0.107 | 0.157 | 0.175 | |
ks03 | 0.167 | 0.276 | 0.096 | 0.183 | 0.276 | 0.175 | 0.166 | 0.107 | 0.159 | 0.178 |
Count: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 7797092 | 4317844 | 5594857 | 5742251 | 5045895 | 2064765 | 1479042 | 2143430 | 1603448 | 3976513 | |
ks03 | 7114036 | 3902664 | 4777574 | 5452444 | 4522585 | 2022989 | 1457203 | 2089632 | 1598331 | 3659717 |
benchmark_360v2_ours_stmt: Single-scale Training and Multi-scale Testing on the the Blender dataset
PSNR: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 27.564 | 23.846 | 29.842 | 28.045 | 24.128 | 33.534 | 30.549 | 34.144 | 33.698 | 29.483 | |
ks03 | 27.264 | 23.564 | 29.664 | 27.936 | 24.164 | 33.294 | 30.242 | 33.600 | 33.107 | 29.204 |
SSIM: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 0.871 | 0.753 | 0.931 | 0.847 | 0.743 | 0.966 | 0.946 | 0.975 | 0.973 | 0.889 | |
ks03 | 0.864 | 0.744 | 0.925 | 0.844 | 0.744 | 0.965 | 0.941 | 0.972 | 0.969 | 0.885 |
LPIPS: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 0.103 | 0.190 | 0.050 | 0.129 | 0.196 | 0.047 | 0.056 | 0.027 | 0.032 | 0.092 | |
ks03 | 0.116 | 0.211 | 0.057 | 0.139 | 0.209 | 0.047 | 0.062 | 0.030 | 0.037 | 0.101 |
Count: | bicycle | flowers | garden | stump | treehill | room | counter | kitchen | bonsai | Average | |
---|---|---|---|---|---|---|---|---|---|---|---|
orig | 5405063 | 3019078 | 2621019 | 4474809 | 4254862 | 1213345 | 921229 | 1286991 | 1286176 | 2720285 | |
ks03 | 4632338 | 2595209 | 2133358 | 4065068 | 3714773 | 1065752 | 821504 | 1251445 | 1170510 | 2383328 |
Thanks for your great work! I understand more about 3dgs from your work!
In 6.1 Implementation,
We choose the variance of our 2D Mip filter as 0.1, approximating a single pixel
My questions: How to deduce from
a single pixel
to0.1
value? By the way, what is the unit of0.1
?In my understande: In computeCov2D function, cov[0][0] += kernel_size; means the scale (or unit) of kernel_size and cov should be the same, because they can add together.
cov
is the covariance in ray space. So the unit of 0.1 should be the unit in ray space. But why 0.1 in $(x_0, x_1)$ plane of ray space occupies a aingle pixel?From Fig. 8 ray space. of EWA Splatting
Thanks!