Skip to yearly menu bar Skip to main content


Poster

Self-supervised Shape Completion via Involution and Implicit Correspondences

Mengya Liu · Ajad Chhatkuli · Janis Postels · Luc Van Gool · Federico Tombari

# 206
Strong blind review: This paper was not made available on public preprint services during the review process Strong Double Blind
[ ] [ Paper PDF ]
Fri 4 Oct 1:30 a.m. PDT — 3:30 a.m. PDT

Abstract: 3D shape completion is traditionally solved using supervised training or by distribution learning on complete shape examples. Recently self-supervised learning approaches that do not require any complete 3D shape examples have gained more interests. In this paper, we propose a non-adversarial self-supervised approach for the shape completion task. Our first finding is that completion problems can be formulated as an involutory function trivially, which implies a special constraint on the completion function $f$, such that $f \circ f(x) = x$. Our second constraint on self-supervised shape completion relies on the fact that shape completion becomes easier to solve with correspondences and similarly, completion can simplify the correspondences problem. We formulate a consistency measure in the canonical space in order to supervise the completion function. We efficiently optimize the completion and correspondence modules using ``freeze and alternate'' strategy. The overall approach performs well for rigid shapes in a category as well as dynamic non-rigid shapes. We ablate our design choices and compare our solution against state-of-the-art methods, showing remarkable accuracy approaching supervised accuracy in some cases.

Live content is unavailable. Log in and register to view live content