\section{Datasets}
\label{sec:datasets}
%%%% important properties
% #shapes, #classes
% humans/animals
% near/non-isometric
% ff, fp, pp
% mesh/pc data
% gt correspondence annotation (dense/sparse?)
% synthetic/real data


For a fair comparison of different approaches, standardized evaluation datasets are essential. This need becomes even more critical as data-driven methods, which rely heavily on large, high-quality datasets, gain popularity. To provide a clear overview, we summarize the datasets relevant to shape correspondence. These datasets vary in multiple aspects, including size, availability of ground truth correspondences, presence of landmarks, and degree of isometry. Table \ref{tab:datasets} lists the relevant datasets along with their key characteristics. Additional datasets are discussed in \cite{deng2022-registrationSurvey}, which can also be used for shape correspondence but have a bigger focus on registration.

\begin{itemize}
    \item \textbf{Size} gives the number of models and classes that are present in the dataset. Typically, data-driven methods rely on a large number of training examples.
    %
    \item \textbf{Ground truth} states whether dense ground truth annotations exist. They can either be computed with a method of choice or be obtained from scan data. Most datasets only give intra-class ground truth.
    %
    \item \textbf{Landmarks} are the sparse counterpart to dense ground truth correspondences. Typically, a dataset has either dense ground truth or sparse landmarks. Landmarks are usually obtained from hand-crafted annotations.
    %
    \item \textbf{Similarity} describes how similar shape pairs are. A correspondence is \emph{isometric} if pairwise geodesic distances are preserved. In practice, however, even for different poses of the same object, pairwise geodesic distances are not exactly preserved. Thus, correspondences between different poses of the same object and correspondences between different objects of the same class are considered \emph{near-isometric}, while correspondences between objects of different classes are \emph{non-isometric}. This classification is heuristic, as there is no universal threshold for distance preservation, and introduces a higher level of abstraction, requiring a decision about class membership. We consider a dataset near-isometric when only intra-class ground truth is provided, even though correspondences between different classes could in principle be computed.
    %
    \item \textbf{URL} provides a link (if available) to the dataset's website or GitHub repository  as of the date of publication.
    %
    \item \textbf{In the description} in the lower line, we provide additional information of the dataset.
    %
    \item \textbf{Difficulty} is indicated by the colored dot, ranging from green (easy) to red (hard). Difficulty depends on factors such as similarity, partiality, variations in density and connectivity, topological artifacts, and non-manifoldness. Although this metric is inherently subjective, it provides a useful initial overview.
\end{itemize}

\begin{table*}[p]
\centering
\begin{tabular}{c|c c||c|c|c|>{\raggedright\arraybackslash}p{2.3cm}|c}
\multicolumn{3}{c||}{\textbf{Name}} & 
\makecell{Size} & 
\makecell{Ground\\Truth} & 
\makecell{Landmarks} & 
\makecell{Similarity} & 
\makecell{URL} \\
\hline\hline

%%%%% only full meshes 
%\multirow{28}{*}{\makecell{full\\shapes}} 
%\parbox[t]{2mm}{\multirow{28}{*}{\rotatebox[origin=c]{90}{full shapes}}}
\multirow{28}{*}{\rotatebox[origin=c]{90}{full shapes}}
% SCAPE
& \multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{SCAPE \cite{anguelov2005dataset-scape}}} & \makecell{71 poses of\\one person} &  \makecell{\ding{51}} & \makecell{ - } & \makecell{isometric} & \makecell{\href{https://ai.stanford.edu/~drago/Projects/scape/scape.html}{Website}} \\
\cline{4-8}
&&& \multicolumn{5}{r}{\makecell{poses of one person based on animation sequences}} \\ 
\cline{2-8}
% TOSCA
& \multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{TOSCA \cite{bronstein2008dataset-tosca}}} & \makecell{ 80 models\\9 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{isometric}
& \makecell{\href{https://github.com/NafieAmrani/becos-code/tree/main/data/tosca-off}{BeCoS GitHub} } \\
% dead link: \href{https://tosca.cs.technion.ac.il/book/resources_data.html}{todocheck}
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{synthetic meshes of animals and humans; compatible triangulations}} \\ 
\cline{2-8}
% FAUST
& \multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{FAUST \cite{bogo2014dataset-faust}}} & \makecell{300 scans of\\10 people} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://faust-leaderboard.is.tuebingen.mpg.de/}{Website}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{different poses}} \\ 
\cline{2-8}
% KIDS
& \multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{KIDS \cite{rodola2014datasets-kids}}} & \makecell{32 shapes\\2 classes} & \makecell{\ding{51}} & \makecell{ - } & \makecell{near-isometric} & \makecell{\href{https://cvg.cit.tum.de/data/datasets/kids}{Website}} \\
\cline{4-8} &&& \multicolumn{5}{l}{\makecell{two different kids; contains self intersections; compatible triangulations}} \\ 
\cline{2-8}
% SURREAL (SMPL)
& \multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{SURREAL SMPL\\\cite{varol2017dataset-surreal, loper2015datasets-smpl, groueix2018-3dcoded}}} & \makecell{230k human shapes\\via SMPL} & \makecell{\ding{51}} & \makecell{ - } & 
% created in 3d coded
\makecell{near-isometric} % todo maybe even isometric
& \makecell{ - } \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{also includes bent humans; \cite{groueix2018-3dcoded} use SMPL \cite{loper2015datasets-smpl} on SURREAL \cite{varol2017dataset-surreal}}} \\ 
% smpl source correct? faust is given in 3dcoded
\cline{2-8}
% DT4D
&\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{DeformingThings4D \\ \cite{li2021datasets-dt4d}}} & \makecell{1972 sequences of\\31 categories} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://github.com/rabbityl/DeformingThings4D}{GitHub}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{animation sequences; synthetic; compatible triangulations}} \\ 
\cline{2-8}
% TOPKIDS
& \multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{TOPKIDS\cite{lahner2016dataset-topkids}}} & \makecell{25 shapes} & \makecell{\ding{51}} & \makecell{ - } & \makecell{near-isometric} & \makecell{\href{https://cvg.cit.tum.de/data/datasets/topkids}{Website}} \\
\cline{4-8} &&& \multicolumn{5}{l}{\makecell{based on KIDS; with topological changes}} \\ 
\cline{2-8}
% SMAL
& \multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{SMAL \cite{zuffi2017dataset-smal}}} & \makecell{49 scans from\\5 classes} & \makecell{ - }  & \makecell{\yes} & 
\makecell{non-isometric}
& \makecell{\href{https://smal.is.tue.mpg.de/}{Website}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{3D animal model; shapes are obtained by fitting the model to images}} \\ 
\cline{2-8}
% SHREC19
& \multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{SHREC'19 \cite{melzi2019dataset-shrec19}}} & \makecell{44 human shapes\\430 shape pairs} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://profs.scienze.univr.it/~marin/shrec19/}{Website}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{not canonically aligned; connectivity artifacts, topological changes, and density variation}} \\ 
\cline{2-8}
% DT4D-Matching
&\multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{DT4D-Matching\\ \cite{magnet2022fm-effDirichlet}}} & \makecell{56 animals, 8 humans\\15-50 poses each} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://github.com/llorz/3DV22_DeformingThings4DMatching_dataset}{GitHub}} \\ 
% ???? cross vs inter category???
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on DeformingThings4D; independent remeshing; cross-category ground truth}} \\ 




\hline \hline
%%%%% full and partial meshes
\multirow{17}{*}{\rotatebox[origin=c]{90}{full and partial shapes}} 
% SHREC16
& \multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{SHREC'16 CUTS/HOLES\\ \cite{cosmo2016dataset-shrec16ch}}} & \makecell{599 models \\ 8 classes} & \makecell{ \ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://pytorch-geometric.readthedocs.io/en/2.5.0/generated/torch_geometric.datasets.SHREC2016.html}{PyTorch}} \\
% link to subproject is dead, vorgänger: https://cvg.cit.tum.de/data/datasets/partial
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on TOSCA; partial meshes via cutting planes and hole growing; full-to-partial}} \\ 
\cline{2-8}
% FARM partial
&\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{FARM partial\\\cite{kirgo2021fm-waveletBasedHeatKernel}}} & \makecell{human shapes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric} % their paper states non isometry
& \makecell{ - } \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on SHREC'19; partiality; different connectivities}} \\ % fp ?
\cline{2-8}
% PFARM
&\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{PFARM \cite{attaiki2021fm-dpfm}}} & \makecell{25 pairs from\\26 human shapes} & \makecell{\ding{51}}  & \makecell{ - } & % 25/26 from website; 27/28 from paper
\makecell{near-isometric}
& \makecell{\href{https://github.com/pvnieo/cp2p-pfarm-benchmark?tab=readme-ov-file\#pfarm}{GitHub}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{extension of FARM partial; varying meshing \& vertex density; partiality (one full shape)}} \\ 
\cline{2-8}
% PFAUST
& \multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{PFAUST \cite{bracha2024dataset-pfaust}}} & \makecell{10 people\\20 poses per person} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://github.com/ABracha/PFAUST}{GitHub}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on FAUST; introduces partiality: 10 full and 10 partial poses per person}} \\ 
\cline{2-8}
% SHREC20
& \multirow{2}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{2}{*}{\makecell{SHREC'20 \cite{dyke2020dataset-shrec20}}} & \makecell{14 models\\of quadrupeds} & \makecell{ - }  & \makecell{\yes} & 
\makecell{non-isometric}
& \makecell{\href{http://robertodyke.com/shrec2020/index2.html}{Website}} \\
\cline{4-8}
&& & \multicolumn{5}{l}{\makecell{test sets with varying difficulty; topological changes (due to self-contact)}} \\ 
\cline{2-8}
% BeCoS
&\multirow{3}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{3}{*}{\makecell{BeCoS Benchmark\\\cite{ehm2024partialBenchmark}}} & \makecell{2543 shapes from\\7 datasets} & \makecell{\ding{51}} & \makecell{ - } & \makecell{non-isometric} & \makecell{\href{https://nafieamrani.github.io/BeCoS/}{Website}\\\href{https://github.com/NafieAmrani/becos-code}{GitHub}} \\
\cline{4-8} &&& \multicolumn{5}{l}{\makecell{framework to create large partial datasets; also partial-to-partial; cross-dataset and \\cross-category correspondences; annotated semantic missing parts in full shapes}} \\ 




\hline \hline
%%%%% only partial meshes
\multirow{5}{*}{\rotatebox[origin=c]{90}{partial shapes}} 
% CP2P
&\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{CP2P \cite{attaiki2021fm-dpfm}}} & \makecell{300 pairs from\\8 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric} % because gt only for intra class, however can also compute between classes
& \makecell{\href{https://github.com/pvnieo/cp2p-pfarm-benchmark}{GitHub}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on SHREC'16 CUTS; partial-to-partial; only intra-class ground truth}} \\ 
\cline{2-8}
% PARTIALSMAL
&\multirow{2}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{2}{*}{\makecell{PARTIALSMAL\\ \cite{ehm2024in-partialGeometricConsistency}}} & \makecell{1274 shapes\\8 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://github.com/vikiehm/gc-ppsm/tree/main}{GitHub}} \\
\cline{4-8}
&&& \multicolumn{5}{l}{\makecell{based on SMAL; inter-class; only 404 g.t. maps; partial-to-partial via cutting planes}} \\ 
% classes: (train) cow, dog, fox, lion, wolf; (test) cougar, hippo, horse, -> 5+3=8




\end{tabular}
\caption{Relevant datasets for shape correspondence.}
\label{tab:datasets}
\end{table*}



\begin{comment}
\begin{table*}[p]
\centering
\begin{tabular}{c c||c|c|c|>{\raggedright\arraybackslash}p{2.3cm}|c}
\multicolumn{2}{c}{\textbf{Name}} & 
\makecell{Size} & 
\makecell{Ground\\Truth} & 
\makecell{Landmarks} & 
\makecell{Similarity} & 
\makecell{URL} \\
\hline\hline
% SCAPE
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{SCAPE \cite{anguelov2005dataset-scape}}} & \makecell{71 poses of\\one person} &  \makecell{\ding{51}} & \makecell{ - } & \makecell{isometric} & \makecell{\href{https://ai.stanford.edu/~drago/Projects/scape/scape.html}{website}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{poses of one person based on animation sequences}} \\ 
\hline
% TOSCA
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{TOSCA \cite{bronstein2008dataset-tosca}}} & \makecell{ 80 models\\9 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{isometric}
& \makecell{ - } \\
% dead link: \href{https://tosca.cs.technion.ac.il/book/resources_data.html}{todocheck}
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{synthetic meshes of animals and humans; compatible triangulations}} \\ 
\hline
% TOSCA Non-Isometric
%\makebox[0pt][l]{\raisebox{-1.2ex}\medium} & \multirow{2}{*}{\makecell{TOSCA Non-Isometric \\ \cite{bronstein2008dataset-tosca}}} & \makecell{ TODO\\size} & \makecell{ \ding{51} (inter?)}  & \makecell{TODO lm} & 
%\makecell{non-isometric}
%& \makecell{ - } \\
%\cline{3-7}
%&& \multicolumn{5}{l}{\makecell{introduces inter-class; ground truth}} \\ 
%\hline
% KIDS
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{KIDS \cite{rodola2014datasets-kids}}} & \makecell{32 shapes\\2 classes} & \makecell{\ding{51}} & \makecell{ - } & \makecell{near-isometric} & \makecell{\href{https://cvg.cit.tum.de/data/datasets/kids}{website}} \\
\cline{3-7} && \multicolumn{5}{l}{\makecell{two different kids; contains self intersections; compatible triangulations}} \\ 
\hline
% TOPKIDS
\multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{TOPKIDS\cite{lahner2016dataset-topkids}}} & \makecell{25 shapes} & \makecell{\ding{51}} & \makecell{ - } & \makecell{near-isometric} & \makecell{\href{https://cvg.cit.tum.de/data/datasets/topkids}{website}} \\
\cline{3-7} && \multicolumn{5}{l}{\makecell{from KIDS; part of SHREC'16; introduces topological changes (to avoid self-intersections)}} \\ 
\hline
% SHREC16
\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{SHREC'16 CUTS/HOLES\\ \cite{cosmo2016dataset-shrec16ch}}} & \makecell{599 models \\ 8 classes} & \makecell{ \ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{ - } \\
% link to subproject is dead, vorgänger: https://cvg.cit.tum.de/data/datasets/partial
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on TOSCA; partial meshes via cutting planes and hole growing; full-to-partial}} \\ 
\hline
% SHREC19
\multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{SHREC'19 \cite{melzi2019dataset-shrec19}}} & \makecell{430 shape pairs from\\44 human shapes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://profs.scienze.univr.it/~marin/shrec19/}{website}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{not aligned orientation; connectivity artifacts, topological changes and density variation}} \\ 
\hline
% SHREC20
\multirow{2}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{2}{*}{\makecell{SHREC'20 \cite{dyke2020dataset-shrec20}}} & \makecell{14 models\\of quadrupeds} & \makecell{ - }  & \makecell{\yes} & 
\makecell{non-isometric}
& \makecell{\href{http://robertodyke.com/shrec2020/index2.html}{website}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{full and partial meshes}} \\ 
\hline
% FAUST
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{FAUST \cite{bogo2014dataset-faust}}} & \makecell{300 scans of\\10 people} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://faust-leaderboard.is.tuebingen.mpg.de/}{website}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{different poses}} \\ 
\hline
% PFAUST
\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{PFAUST \cite{bracha2024dataset-pfaust}}} & \makecell{10 people\\20 poses per person} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://github.com/ABracha/PFAUST}{github}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on FAUST; introduces partiality: 10 full and 10 partial poses per person}} \\ 
\hline
% SMAL
\multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{SMAL \cite{zuffi2017dataset-smal}}} & \makecell{49 scans from\\5 classes} & \makecell{ - }  & \makecell{\yes} & 
\makecell{non-isometric}
& \makecell{\href{https://smal.is.tue.mpg.de/}{website}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{3D animal model; shapes are obtained by fitting the model to images}} \\ 
\hline
% scans of toy animals, inter class landmarks
% 49 from 8 is from \cite{ehm2024in-partialGeometricConsistency}, 41 from 13 is from own paper
% PARTIALSMAL
\multirow{2}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{2}{*}{\makecell{PARTIALSMAL\\ \cite{ehm2024in-partialGeometricConsistency}}} & \makecell{1274 shapes\\8 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://github.com/vikiehm/gc-ppsm/tree/main}{github}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on SMAL; inter-class; only 404 ground truth maps; partial-to-partial via cutting planes}} \\ 
% classes: (train) cow, dog, fox, lion, wolf; (test) cougar, hippo, horse, -> 5+3=8
\hline
% SURREAL (SMPL)
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{SURREAL SMPL\\\cite{varol2017dataset-surreal, loper2015datasets-smpl, groueix2018-3dcoded}}} & \makecell{230k human shapes\\via SMPL} & \makecell{\ding{51}} & \makecell{ - } & 
% created in 3d coded
\makecell{near-isometric} % todo maybe even isometric
& \makecell{ - } \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{also includes bent humans; \cite{groueix2018-3dcoded} use SMPL \cite{loper2015datasets-smpl} on SURREAL \cite{varol2017dataset-surreal}}} \\ 
% smpl source correct? faust is given in 3dcoded
\hline
% FARM partial
\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{FARM partial\\\cite{kirgo2021fm-waveletBasedHeatKernel}}} & \makecell{human shapes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric} % their paper states non isometry
& \makecell{ - } \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on SHREC'19; partiality; extremely different connectivities}} \\ % fp ?
\hline
% PFARM
\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{PFARM \cite{attaiki2021fm-dpfm}}} & \makecell{25 pairs from\\26 human shapes} & \makecell{\ding{51}}  & \makecell{ - } & % 25/26 from website; 27/28 from paper
\makecell{near-isometric}
& \makecell{\href{https://github.com/pvnieo/cp2p-pfarm-benchmark}{github}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{extension of FARM partial; varying meshing \& vertex density; partiality (one full shape)}} \\ 
\hline
% CP2P
\multirow{2}{*}{\makebox[0pt][l]{\hard}} & \multirow{2}{*}{\makecell{CP2P \cite{attaiki2021fm-dpfm}}} & \makecell{300 pairs from\\8 classes} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric} % because gt only for intra class, however can also compute between classes
& \makecell{\href{https://github.com/pvnieo/cp2p-pfarm-benchmark}{github}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on SHREC'16 CUTS; partial-to-partial; only intra class ground truth}} \\ 
\hline
% DT4D
\multirow{2}{*}{\makebox[0pt][l]{\easy}} & \multirow{2}{*}{\makecell{DeformingThings4D \\ \cite{li2021datasets-dt4d}}} & \makecell{1972 sequences of\\31 categories} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{near-isometric}
& \makecell{\href{https://github.com/rabbityl/DeformingThings4D}{github}} \\
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{animation sequences; synthetic; compatible triangulations}} \\ 
\hline
% DT4D-Matching
\multirow{2}{*}{\makebox[0pt][l]{\medium}} & \multirow{2}{*}{\makecell{DT4D-Matching\\ \cite{magnet2022fm-effDirichlet}}} & \makecell{56 animals, 8 humans\\15-50 poses each} & \makecell{\ding{51}}  & \makecell{ - } & 
\makecell{non-isometric}
& \makecell{\href{https://github.com/llorz/3DV22_DeformingThings4DMatching_dataset}{github}} \\ 
% ???? cross vs inter category???
\cline{3-7}
&& \multicolumn{5}{l}{\makecell{based on DeformingThings4D; independent remeshing; cross-category ground truth}} \\ 
\hline
% BeCoS
\multirow{3}{*}{\makebox[0pt][l]{\veryhard}} & \multirow{3}{*}{\makecell{BeCoS Benchmark\\\cite{ehm2024partialBenchmark}}} & \makecell{2543 shapes from\\7 datasets} & \makecell{\ding{51}} & \makecell{ - } & \makecell{non-isometric} & \makecell{\href{https://nafieamrani.github.io/BeCoS/}{website}\\\href{https://github.com/NafieAmrani/becos-code}{github}} \\
\cline{3-7} && \multicolumn{5}{l}{\makecell{framework to create large partial (training) datasets;\\cross-dataset and cross-category correspondences}} \\ 
\hline
%
%\makebox[0pt][l]{\raisebox{-1.2ex}\easy} & \multirow{2}{*}{\makecell{TODO title and source}} & \makecell{TODO\\size} & \makecell{ TODO gt} & \makecell{TODO landmarks} & \makecell[l]{TODO iso?} & \makecell{\href{https://www.google.com/}{TODO url}} \\
%\cline{3-7} && \multicolumn{5}{l}{\makecell{TODO Description}} \\ 
%\hline
\end{tabular}
\caption{This table lists relevant datasets for shape correspondence.}
\label{tab:datasets}
\end{table*}
\end{comment}




%
%\multirow{2}{*}{\makecell{SCAPE \cite{anguelov2005dataset-scape}}} & - &  \makecell{ - } & \makecell{ - } & \makecell{} & \makecell{ - } \\
%\cline{2-6}
%& \multicolumn{5}{l}{\makecell{poses of person based  on animation sequences}} \\ 
%\hline
%\multirow{2}{*}{\makecell{TOSCA \cite{bronstein2008dataset-tosca}}} & \makecell{ 80 models\\9 classes} & \makecell{ TODO gt}  & \makecell{TODO landmarks} & 
%\begin{tabular}{@{}l@{}l@{}} 
%\easy& 
%\makecell[l]{isometric}
%\end{tabular}
%& \makecell{ TODO URL } \\
%\cline{2-6}
%& \multicolumn{5}{l}{\makecell{synthetic meshes of animals and humans}} \\ 
% DeformingThings4D-Matching paper describes non-isometric tosca
% additional source https://paperswithcode.com/dataset/tosca
%
%\hline
%\multirow{2}{*}{\makecell{TOSCA Non-Isometric \\ \cite{bronstein2008dataset-tosca}}} & \makecell{ TODO size} & \makecell{ \ding{51}}  & \makecell{TODO landmarks} & 
%\begin{tabular}{@{}l@{}l@{}} 
%\medium& 
%\makecell[l]{non-isometric}
%\end{tabular}
%& \makecell{ TODO URL } \\
%\cline{2-6}
%& \multicolumn{5}{l}{\makecell{introduces inter-class; ground truth}} \\ 
%&  \makecell{introduces inter-class\\ground truth} & \makecell{-} &  \makecell{ yes } & \makecell{ - } & \makecell{non-isometric} & \makecell{ - } \\
% mentioned in \cite{magnet2022fm-effDirichlet}, but when was it exactly created?
% also in \cite{kirgo2021fm-waveletBasedHeatKernel}
%\hline
%\makecell{SHREC16\\CUTS/HOLES\\ \cite{cosmo2016dataset-shrec16ch}} &  \makecell{based on TOSCA \\  Partial meshes  } & \makecell{599 models \\ 8 classes} &  \makecell{ yes } & \makecell{ - } & \makecell{ - } & \makecell{ - } \\
%\hline
%\makecell{SHREC19 \\ \cite{melzi2019dataset-shrec19}} &  \makecell{not aligned orientation,\\ connectivity artifacts \\ and density variation } & \makecell{430 shape pairs \\ from 44 human \\ shapes} &  \makecell{yes} & \makecell{ - } & \makecell{ - } & \makecell{ - } \\
%\hline
%
%\makecell{SHREC20 \\ \cite{dyke2020dataset-shrec20}} &  \makecell{full and partial\\meshes} & \makecell{14 models\\of quadrupeds} &  \makecell{-} & \makecell{hand-crafted} & \makecell{ non-isometric } & \makecell{ - } \\\hline
%
%\makecell{FAUST \\ \cite{bogo2014dataset-faust}} &  \makecell{scans of 10 people\\ in different poses} & \makecell{300 scans of\\10 people} &  \makecell{computed} & \makecell{ - } & \makecell{near-isometric} & \makecell{ - } \\
%\hline
%
%\makecell{PFAUST \\ \cite{bracha2024dataset-pfaust}} &  \makecell{based on FAUST\\introduces partiality} & \makecell{20 poses of \\10 people} &  \makecell{computed} & \makecell{ - } & \makecell{ - } & \makecell{ - } \\
%\hline
%
%\makecell{SMAL \\ \cite{zuffi2017dataset-smal}} &  \makecell{scans of\\toy animals} & \makecell{49 animals\\from 8 species\\or 41 from 13 ?} &  \makecell{ - } & \makecell{36 hand-clicked\\keypoints} & \makecell{near-isometric} & \makecell{https://\\smal.is.tue\\.mpg.de/} \\
% 49 from 8 is from \cite{ehm2024in-partialGeometricConsistency}
%\hline
%
%\makecell{PARTIALSMAL\\\cite{ehm2024in-partialGeometricConsistency}} & \makecell{inter-class,\\partial-to-partial\\via cutting planes} & \makecell{TODO size} &  \makecell{TODO gt} & \makecell{TODO lm} & \makecell{non-isometric} & \makecell{url} \\
%\hline
%
%\makecell{CP2P\\\cite{attaiki2021fm-dpfm}} & \makecell{from SHREC16 CUTS\\partial-to-partial} & \makecell{300 pairs from\\8 classes} &  \makecell{yes} & \makecell{ - } & \makecell{TODO simi} & \makecell{url} \\ % gt correspondence only intra class
%\hline
%
%\makecell{FARM\\partial\\\cite{kirgo2021fm-waveletBasedHeatKernel}} &  \makecell{partiality,\\extremely different\\connectivities} & \makecell{TODO size} &  \makecell{computed} & \makecell{ - } & \makecell{non-isometric} & \makecell{url} \\
%\hline
%
%\makecell{PFARM\\\cite{attaiki2021fm-dpfm}} & \makecell{extension of FARM partial\\varying meshing\\\& vertex density\\partiality} & \makecell{27 test pairs from\\28 human shapes\\(one null shape)?} &  \makecell{computed} & \makecell{ - } & \makecell{non-isometric} & \makecell{url} \\
%\hline
%
%\makecell{Deforming \\ Things4D \\ \cite{li2021datasets-dt4d} } &  \makecell{animation sequences \\synthetic} & \makecell{1972 sequences of\\31 categories} &  \makecell{compatible \\ triangulations} & \makecell{ - } & \makecell{near-isometric} & \makecell{ - } \\
%\hline
%
%\makecell{Deforming \\ Things4D \\ -Matching\\ \cite{magnet2022fm-effDirichlet}} &  \makecell{based on\\DeformingThings4D\\independent remeshing} & \makecell{56 animal categories\\ 8 humanoid\\15-50 poses\\per category} &  \makecell{cross-category\\ground truth} & \makecell{ - } & \makecell{non-isometric} & \makecell{ - } \\



\color{red}

%The SCAPE dataset \cite{anguelov2005dataset-scape} contains different poses of one person based on animation sequences.
%Another early 3D shape correspondence dataset was introduced by \cite{bronstein2008dataset-tosca}, called TOSCA. It contains synthetic meshes of both animals and humans.
% SHREC
%The 3D Shape Retrieval Challenge SHREC creates annual sets of benchmarks that have contained tasks in the area of shape correspondence in the past. The most relevant are listed in the following. 
%SHREC16 CUTS/HOLES \cite{cosmo2016dataset-shrec16ch} is a dataset based on TOSCA that contains large amounts of partiality with ground truth correspondences. The original meshes were remeshed to avoid compatible triangulations. It contains 599 models in 8 classes, buth humans and animals. Every class has one complete template shape that is the used for full-to-partial matching. Partial meshes are obtained by cutting planes or growing holes.
%SHREC19 \cite{melzi2019dataset-shrec19} contains 430 shape pairs from 44 human shapes with dense ground truth correspondences obtained via the FARM registration method \cite{marin2020datasets-farm}. It is based on various other datasets such as SCAPE, TOSCA and FAUST. Its challenges include not aligned orientation, connectivity artifacts and density variation.
%SHREC20 \cite{dyke2020dataset-shrec20} consists of 14 models of quadrupeds with manually obtained sparse ground truth correspondences. It contains full as well as partial meshes. The variance of species results in non-isometric correspondence problems.

%FAUST \cite{bogo2014dataset-faust} contains 300 scans of 10 people in different poses. It has automatically computed ground-truth correspondences.
%PFAUST \cite{bracha2024dataset-pfaust} is a FAUST derived data set that introduces partiality by adding holes. For the 10 people, there are 20 poses, with 10 of them being only partial. The is a medium and a hard difficulty version, which differ in the amount of holes introduced.

%\begin{itemize}
%    \item SCAPE \cite{anguelov2005dataset-scape} -> humans, 71 poses of one person, near-isometric, ff
%    \item TOSCA \cite{bronstein2008dataset-tosca} -> synthetic, mesh data, ff
    
%    \item FAUST \cite{bogo2014dataset-faust} -> 300 (100 training, 200 test) scans of 10 people, real data, 100 pairs (60 intra, 40 inter-subject), dense gt correspondences, humans, ff, meshes
    
%    \item SHREC16 cuts/holes \cite{cosmo2016dataset-shrec16ch} -> 599 models, 8 classes, fp, humans and animals, near-isometric, one "template shape" per class, dense gt matches (vertex-to-point), meshes (remeshed to 10k verts), based on TOSCA (synthetic)
%    \item SHREC19 \cite{melzi2019dataset-shrec19} -> 44 shapes, 430 shape pairs, dense correspondence annotation (from FARM method), humans (-> near isometric), ff (but not all watertight?), density variation, mesh data, both real and synthetic
%    \item SHREC20 \cite{dyke2020dataset-shrec20} -> animals (quadrupeds), non-isometric, sparse gt (annotated by specialists), both synthetic and real, 14 models, ff, fp, mesh data
%    \item PFAUST \cite{bracha2024dataset-pfaust} -> ff, fp, divided into -M and -H (medium/hard), 10 people, 20 poses for each (10 full, 10 partial), humans, near-isometric, dense gt correspondence, derived from FAUST
%\item SMAL \cite{zuffi2017dataset-smal} -> ssm of 3d scans of animal figurines, 49 animals from 8 species, ff


%\item DeformingThings4D \cite{li2021datasets-dt4d} -> 1972 animation sequences, 31 categories (animals and humanoid), near-isometric, synthetic (from real scans?) (but named synthetic in effDirichlet paper), compatible triangulations, ff

%    \item SURREAL \cite{varol2017dataset-surreal} only images
    
%    \item DeformingThings4D-Matching \cite{magnet2022fm-effDirichlet} -> 56 animal categories, 8 humanoid, 15-50 snapshots per shape, independent remeshing, create cross-category correspondences, non-isometric, ff
   
%    \item PARIALSMAL \cite{ehm2024in-partialGeometricConsistency} -> inter-class, non-isometric, pp
%    \item CP2P \cite{attaiki2021fm-dpfm} -> based on shrec16, intra-class, pp, 
%    \item FARM partial \cite{kirgo2021fm-waveletBasedHeatKernel}
%    \item PFARM \cite{attaiki2021fm-dpfm} -> based on farm, fp
%    \item 
%\end{itemize}

\color{black}