Signed distance functions (SDFs) is an attractive framework that has recently
shown promising results for 3D shape reconstruction from images. SDFs
seamlessly generalize to different shape resolutions and topologies but lack
explicit modelling of the underlying 3D geometry. In this work, we exploit the
hand structure and use it as guidance for SDF-based shape reconstruction. In
particular, we address reconstruction of hands and manipulated objects from
monocular RGB images. To this end, we estimate poses of hands and objects and
use them to guide 3D reconstruction. More specifically, we predict kinematic
chains of pose transformations and align SDFs with highly-articulated hand
poses. We improve the visual features of 3D points with geometry alignment and
further leverage temporal information to enhance the robustness to occlusion
and motion blurs. We conduct extensive experiments on the challenging ObMan and
DexYCB benchmarks and demonstrate significant improvements of the proposed
method over the state of the art.

本文利用手的结构作为 SDF 的引导，并预测姿势变换的运动链，对高关节手姿势对齐 SDF 进行了改进，最终在 ObMan 和 DexYCB 基准测试中取得了比现有技术更显著的改进。

gSDF: 基于几何的有符号距离函数在 3D 手部物体重建中的应用

gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object  Reconstruction

Inverse kinematics - finding joint poses that reach a given Cartesian-space
end-effector pose - is a common operation in robotics, since goals and
waypoints are typically defined in Cartesian space, but robots must be
controlled in joint space. However, existing inverse kinematics solvers return
a single solution pose, where systems with more than 6 degrees of freedom
support infinitely many such solutions, which can be useful in the presence of
constraints, pose preferences, or obstacles. We introduce a method that uses a
deep neural network to learn to generate a diverse set of samples from the
solution space of such kinematic chains. The resulting samples can be generated
quickly (2000 solutions in under 10ms) and accurately (to within 10 millimeters
and 2 degrees of an exact solution) and can be rapidly refined by classical
methods if necessary.

本文介绍了使用深度神经网络从运动链的解空间中生成多样化解的方法，能够准确快速地生成解决方案。