Computed Tomography (CT) is pivotal in industrial quality control and medical
diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces
challenges due to its under-sampled nature, leading to ill-posed reconstruction
problems. Recent advancements in Implicit Neural Representations (INRs) have
shown promise in addressing sparse-view CT reconstruction. Recognizing that CT
often involves scanning similar subjects, we propose a novel approach to
improve reconstruction quality through joint reconstruction of multiple objects
using INRs. This approach can potentially leverage both the strengths of INRs
and the statistical regularities across multiple objects. While current INR
joint reconstruction techniques primarily focus on accelerating convergence via
meta-initialization, they are not specifically tailored to enhance
reconstruction quality. To address this gap, we introduce a novel INR-based
Bayesian framework integrating latent variables to capture the inter-object
relationships. These variables serve as a dynamic reference throughout the
optimization, thereby enhancing individual reconstruction fidelity. Our
extensive experiments, which assess various key factors such as reconstruction
quality, resistance to overfitting, and generalizability, demonstrate
significant improvements over baselines in common numerical metrics. This
underscores a notable advancement in CT reconstruction methods.

通过联合表示以及贝叶斯框架，改进了计算机断层扫描（Computed Tomography）重建质量，实验表明明显提高了重建方法。

鲁棒联合稀疏视图 CT 重建的隐式神经表示

Implicit Neural Representations for Robust Joint Sparse-View CT  Reconstruction

Human-object contact serves as a strong cue to understand how humans
physically interact with objects. Nevertheless, it is not widely explored to
utilize human-object contact information for the joint reconstruction of 3D
human and object from a single image. In this work, we present a novel joint 3D
human-object reconstruction method (CONTHO) that effectively exploits contact
information between humans and objects. There are two core designs in our
system: 1) 3D-guided contact estimation and 2) contact-based 3D human and
object refinement. First, for accurate human-object contact estimation, CONTHO
initially reconstructs 3D humans and objects and utilizes them as explicit 3D
guidance for contact estimation. Second, to refine the initial reconstructions
of 3D human and object, we propose a novel contact-based refinement Transformer
that effectively aggregates human features and object features based on the
estimated human-object contact. The proposed contact-based refinement prevents
the learning of erroneous correlation between human and object, which enables
accurate 3D reconstruction. As a result, our CONTHO achieves state-of-the-art
performance in both human-object contact estimation and joint reconstruction of
3D human and object. The code is publicly available at
this https URL

该研究提出了一种利用人 - 物接触信息进行三维人体与物体联合重建的新方法 (CONTHO)，通过 3D 引导接触估计和基于接触的三维人体与物体细化，实现了更准确的重建，取得了最先进的性能。

基于接触优化的转换器进行 3D 人体和物体联合重建

Joint Reconstruction of 3D Human and Object via Contact-Based Refinement  Transformer

Recent work achieved impressive progress towards joint reconstruction of
hands and manipulated objects from monocular color images. Existing methods
focus on two alternative representations in terms of either parametric meshes
or signed distance fields (SDFs). On one side, parametric models can benefit
from prior knowledge at the cost of limited shape deformations and mesh
resolutions. Mesh models, hence, may fail to precisely reconstruct details such
as contact surfaces of hands and objects. SDF-based methods, on the other side,
can represent arbitrary details but are lacking explicit priors. In this work
we aim to improve SDF models using priors provided by parametric
representations. In particular, we propose a joint learning framework that
disentangles the pose and the shape. We obtain hand and object poses from
parametric models and use them to align SDFs in 3D space. We show that such
aligned SDFs better focus on reconstructing shape details and improve
reconstruction accuracy both for hands and objects. We evaluate our method and
demonstrate significant improvements over the state of the art on the
challenging ObMan and DexYCB benchmarks.

本文提出了一种基于先验知识的手部和物体联合重建方法，其中通过参数化模型获得手和物体的姿态，用它们来对齐三维空间中的有符号距离场数据，以重建更精细的手势和形状细节，实验表明该方法在 ObMan 和 DexYCB 数据集上有显著的性能优势。