From c2fa2c81a38c3c8a0a4ffdf06378a4e48bef0a0e Mon Sep 17 00:00:00 2001 From: "Milena T. Bagdasarian" Date: Mon, 9 Sep 2024 14:47:41 +0200 Subject: [PATCH] added .bbl file for survey and pdf for a quick look --- .../latex/3dgs_compression_survey.bbl | 131 ++++++++++++++++++ .../latex/3dgs_compression_survey.pdf | Bin 0 -> 8024348 bytes 2 files changed, 131 insertions(+) create mode 100644 data_extraction/latex/3dgs_compression_survey.bbl create mode 100644 data_extraction/latex/3dgs_compression_survey.pdf diff --git a/data_extraction/latex/3dgs_compression_survey.bbl b/data_extraction/latex/3dgs_compression_survey.bbl new file mode 100644 index 0000000..26b8c55 --- /dev/null +++ b/data_extraction/latex/3dgs_compression_survey.bbl @@ -0,0 +1,131 @@ +\begin{thebibliography}{10} + +\bibitem{MipNeRF360} +J.~T. Barron, B.~Mildenhall, D.~Verbin, P.~P. Srinivasan, and P.~Hedman. +\newblock Mip-nerf 360: Unbounded anti-aliased neural radiance fields. +\newblock In {\em Proceedings of the IEEE/CVF Conference on Computer Vision and + Pattern Recognition}, pages 5470--5479, 2022. + +\bibitem{chen2024hac} +Y.~Chen, Q.~Wu, J.~Cai, M.~Harandi, and W.~Lin. +\newblock Hac: Hash-grid assisted context for 3d gaussian splatting + compression, 2024, 2403.14530. + +\bibitem{chen2024far} +Y.~Chen, Q.~Wu, M.~Harandi, and J.~Cai. +\newblock How far can we compress instant-ngp-based nerf? +\newblock In {\em Proceedings of the IEEE/CVF Conference on Computer Vision and + Pattern Recognition}, pages 20321--20330, 2024. + +\bibitem{fan2024lightgaussian} +Z.~Fan, K.~Wang, K.~Wen, Z.~Zhu, D.~Xu, and Z.~Wang. +\newblock Lightgaussian: Unbounded 3d gaussian compression with 15x reduction + and 200+ fps, 2024, 2311.17245. + +\bibitem{fei20243d} +B.~Fei, J.~Xu, R.~Zhang, Q.~Zhou, W.~Yang, and Y.~He. +\newblock 3d gaussian splatting as new era: A survey. +\newblock {\em IEEE Transactions on Visualization and Computer Graphics}, 2024. + +\bibitem{girish2024eagles} +S.~Girish, K.~Gupta, and A.~Shrivastava. +\newblock Eagles: Efficient accelerated 3d gaussians with lightweight + encodings, 2024, 2312.04564. + +\bibitem{DeepBlending} +P.~Hedman, J.~Philip, T.~Price, J.-M. Frahm, G.~Drettakis, and G.~Brostow. +\newblock Deep blending for free-viewpoint image-based rendering. +\newblock {\em ACM Transactions on Graphics (ToG)}, 37(6):1--15, 2018. + +\bibitem{hu2024gsplat} +J.~Hu, R.~Li, V.~Ye, and A.~Kanazawa. +\newblock gsplat compression, 2024. + +\bibitem{kerbl3Dgaussians} +B.~Kerbl, G.~Kopanas, T.~Leimk{\"u}hler, and G.~Drettakis. +\newblock 3d gaussian splatting for real-time radiance field rendering. +\newblock {\em ACM Transactions on Graphics}, 42(4), July 2023. + +\bibitem{TanksAndTemples} +A.~Knapitsch, J.~Park, Q.-Y. Zhou, and V.~Koltun. +\newblock Tanks and temples: Benchmarking large-scale scene reconstruction. +\newblock {\em ACM Transactions on Graphics}, 36(4), 2017. + +\bibitem{lee2024compact} +J.~C. Lee, D.~Rho, X.~Sun, J.~H. Ko, and E.~Park. +\newblock Compact 3d gaussian representation for radiance field, 2024. + +\bibitem{li2023compressing} +L.~Li, Z.~Shen, Z.~Wang, L.~Shen, and L.~Bo. +\newblock Compressing volumetric radiance fields to 1 mb. +\newblock In {\em Proceedings of the IEEE/CVF Conference on Computer Vision and + Pattern Recognition}, pages 4222--4231, 2023. + +\bibitem{lu2024scaffold} +T.~Lu, M.~Yu, L.~Xu, Y.~Xiangli, L.~Wang, D.~Lin, and B.~Dai. +\newblock Scaffold-gs: Structured 3d gaussians for view-adaptive rendering, + 2024. + +\bibitem{mildenhall2020nerf} +B.~Mildenhall, P.~P. Srinivasan, M.~Tancik, J.~T. Barron, R.~Ramamoorthi, and + R.~Ng. +\newblock Nerf: Representing scenes as neural radiance fields for view + synthesis. +\newblock In {\em ECCV}, 2020. + +\bibitem{SyntheticNeRF} +B.~Mildenhall, P.~P. Srinivasan, M.~Tancik, J.~T. Barron, R.~Ramamoorthi, and + R.~Ng. +\newblock Nerf: Representing scenes as neural radiance fields for view + synthesis. +\newblock {\em Communications of the ACM}, 65(1):99--106, 2021. + +\bibitem{morgenstern2024compact} +W.~Morgenstern, F.~Barthel, A.~Hilsmann, and P.~Eisert. +\newblock Compact 3d scene representation via self-organizing gaussian grids, + 2024, 2312.13299. + +\bibitem{navaneet2023compact3d} +K.~Navaneet, K.~P. Meibodi, S.~A. Koohpayegani, and H.~Pirsiavash. +\newblock Compact3d: Compressing gaussian splat radiance field models with + vector quantization, 2024, 2311.18159. + +\bibitem{niedermayr2024compressed} +S.~Niedermayr, J.~Stumpfegger, and R.~Westermann. +\newblock Compressed 3d gaussian splatting for accelerated novel view + synthesis, 2024. + +\bibitem{papantonakis2024reducing} +P.~Papantonakis, G.~Kopanas, B.~Kerbl, A.~Lanvin, and G.~Drettakis. +\newblock Reducing the memory footprint of 3d gaussian splatting. +\newblock {\em Proceedings of the ACM on Computer Graphics and Interactive + Techniques}, 7(1):1--17, May 2024. + +\bibitem{sun2024f3dgs} +X.~Sun, J.~C. Lee, D.~Rho, J.~H. Ko, U.~Ali, and E.~Park. +\newblock F-3dgs: Factorized coordinates and representations for 3d gaussian + splatting, 2024, 2405.17083. + +\bibitem{wang2024end} +H.~Wang, H.~Zhu, T.~He, R.~Feng, J.~Deng, J.~Bian, and Z.~Chen. +\newblock End-to-end rate-distortion optimized 3d gaussian representation, + 2024, 2406.01597. + +\bibitem{wu2024implicit} +M.~Wu and T.~Tuytelaars. +\newblock Implicit gaussian splatting with efficient multi-level tri-plane + representation. +\newblock {\em arXiv preprint arXiv:2408.10041}, 2024. + +\bibitem{wu2024recent} +T.~Wu, Y.-J. Yuan, L.-X. Zhang, J.~Yang, Y.-P. Cao, L.-Q. Yan, and L.~Gao. +\newblock Recent advances in 3d gaussian splatting. +\newblock {\em Computational Visual Media}, pages 1--30, 2024. + +\bibitem{xie2024mesongs} +S.~Xie, W.~Zhang, C.~Tang, Y.~Bai, R.~Lu, S.~Ge, and Z.~Wang. +\newblock Mesongs: Post-training compression of 3d gaussians via efficient + attribute transformation. +\newblock In {\em European Conference on Computer Vision}. Springer, 2024. + +\end{thebibliography} diff --git a/data_extraction/latex/3dgs_compression_survey.pdf b/data_extraction/latex/3dgs_compression_survey.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6506e29bb2a1350b1a72992d3a41de28c985b131 GIT binary patch literal 8024348 zcmdqIV~}J~(=OWXX=~cnv~5q@wr$(CZM&y!+vc=w+deh#y%FyhaZbcNzi<7h*tKhC z?v*Q_eDYbVc9F^niO?|6vOts0{vBF@=3peCC$KfJfad0grjs(ZF?BK{U}R)uCiu?_ znoh*r%E{P)fKJ3p-^p0W*wEI<7@C(C+R@3uSl=4jZLL^M(s7*~x%*i4Moc-KxtGmU zcG?8T(>1|acx}9IA5=&HdC7|$$ZX{6aXlKsn4<8n)MXA3Gv4P{yEvK^D4Lho$d{2< zkB6&!whA!ae$cUc?HZUfRw)ZYL_vhRHe>~BE5ZT-e(G(-+IM{zqfW4DB>ADa*{{z= zu?V^DSq~gDDZbekd8uLY-9HoFWR=PTDJj1A?_DXi2rYOj-#^@v2cwSplWtm%P;Wm; zE^V}gdJ9?H&JgYbZ=8E~MRLf2!6DmF0$=$Jo0E<_Pp+sbv&xL1ji{UXAV3?+7?KUHGl8(0T=>a2ei@>0LH4S%s0w z)E`q@Nse2!Tnm4Jaz!_8jyYPD6CJJoVMHI}5TTcLw~tv^{0%`pfW4Tm1AAPllFU%^ z@@>v+UR-imV}W!#D`q{DULA1$T_J&CC$E5RKTM$HhPA9N?rC5a{;EpK)k8iYTevme z=VxRZNE)0pCYE+4X6wdLv6tn4j;z;L-g9K&d*LBZnvb?nD@YXugJ)w$q+ifQR)p1P 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