HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed
  Distance Fields
Haozhe Qi Chen Zhao Mathieu Salzmann Alexander Mathis
Human hands are highly articulated and versatile at handling objects. Jointly
estimating the 3D poses of a hand and the object it manipulates from a
monocular camera is challenging due to frequent occlusions. Thus, existing
methods often rely on intermediate 3D shape representations to increase
performance. These representations are typically explicit, such as 3D point
clouds or meshes, and thus provide information in the direct surroundings of
the intermediate hand pose estimate. To address this, we introduce HOISDF, a
Signed Distance Field (SDF) guided hand-object pose estimation network, which
jointly exploits hand and object SDFs to provide a global, implicit
representation over the complete reconstruction volume. Specifically, the role
of the SDFs is threefold: equip the visual encoder with implicit shape
information, help to encode hand-object interactions, and guide the hand and
object pose regression via SDF-based sampling and by augmenting the feature
representations. We show that HOISDF achieves state-of-the-art results on
hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available
at https://github.com/amathislab/HOISDF