We propose Medial Atom Ray Fields (MARFs), a novel neural object
representation that enables accurate differentiable surface rendering with a
single network evaluation per camera ray. Existing neural ray fields struggle
with multi-view consistency and representing surface discontinuities. MARFs
address both using a medial shape representation, a dual representation of
solid geometry that yields cheap geometrically grounded surface normals, in
turn enabling computing analytical curvature despite the network having no
second derivative. MARFs map a camera ray to multiple medial intersection
candidates, subject to ray-sphere intersection testing. We illustrate how the
learned medial shape quantities applies to sub-surface scattering, part
segmentation, and aid representing a space of articulated shapes. Able to learn
a space of shape priors, MARFs may prove useful for tasks like shape retrieval
and shape completion, among others. Code and data can be found at
this https URL

本文提出了一种新的神经物体表示方法 ——MEDIAL ATOM RAY FIELDS (MARFs)，使用单个网络评估实现精确可微分表面渲染，MARFs 地址了使用介质形状表示法的多视图一致性和表面间断的问题，并提供了便宜的地理基准表面法线，MARFs 映射一个相机光线到多个介质交点候选者，这可能会对形状检索和形状完成等任务有用。

MARF：中心原子射线场物体表示法

MARF: The Medial Atom Ray Field Object Representation

3D object reconstruction is important for semantic scene understanding. It is
challenging to reconstruct detailed 3D shapes from monocular images directly
due to a lack of depth information, occlusion and noise. Most current methods
generate deterministic object models without any awareness of the uncertainty
of the reconstruction. We tackle this problem by leveraging a neural object
representation which learns an object shape distribution from large dataset of
3d object models and maps it into a latent space. We propose a method to model
uncertainty as part of the representation and define an uncertainty-aware
encoder which generates latent codes with uncertainty directly from individual
input images. Further, we propose a method to propagate the uncertainty in the
latent code to SDF values and generate a 3d object mesh with local uncertainty
for each mesh component. Finally, we propose an incremental fusion method under
a Bayesian framework to fuse the latent codes from multi-view observations. We
evaluate the system in both synthetic and real datasets to demonstrate the
effectiveness of uncertainty-based fusion to improve 3D object reconstruction
accuracy.

通过神经对象表示学习对象形状分布并将其映射到潜在空间，我们利用不确定性感知编码器直接从单个输入图像生成具有不确定性的潜在代码以模拟 3D 物体重建领域中的不确定性，并在贝叶斯框架下提出增量融合方法来融合多视角观测的潜在编码，从而提高 3D 对象重建的准确性。