SHED: Shape edit distance for fine-grained shape similarity

Yanir Kleiman, Oliver Van Kaick, Olga Sorkine-Hornung, Daniel Cohen-Or

Research output: Contribution to journalArticlepeer-review


Computing similarities or distances between 3D shapes is a crucial building block for numerous tasks, including shape retrieval, exploration and classification. Current state-of-the-art distance measures mostly consider the overall appearance of the shapes and are less sensitive to fine changes in shape structure or geometry. We present shape edit distance (SHED) that measures the amount of effort needed to transform one shape into the other, in terms of rearranging the parts of one shape to match the parts of the other shape, as well as possibly adding and removing parts. The shape edit distance takes into account both the similarity of the overall shape structure and the similarity of individual parts of the shapes. We show that SHED is favorable to state-of-the-art distance measures in a variety of applications and datasets, and is especially successful in scenarios where detecting fine details of the shapes is important, such as shape retrieval and exploration.

Original languageEnglish
Article number235
JournalACM Transactions on Graphics
Issue number6
StatePublished - Nov 2015


  • Edit distance
  • Intra-class retrieval
  • Shape similarity


Dive into the research topics of 'SHED: Shape edit distance for fine-grained shape similarity'. Together they form a unique fingerprint.

Cite this