Scene representation networks (SRNs) have been recently proposed for
compression and visualization of scientific data. However, state-of-the-art
SRNs do not adapt the allocation of available network parameters to the complex
features found in scientific data, leading to a loss in reconstruction quality.
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN)
and propose a domain decomposition training and inference technique for
accelerated parallel training on multi-GPU systems. We also release an
open-source neural volume rendering application that allows plug-and-play
rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses
multiple spatially adaptive feature grids that learn where to be placed within
the domain to dynamically allocate more neural network resources where error is
high in the volume, improving state-of-the-art reconstruction accuracy of SRNs
for scientific data without requiring expensive octree refining, pruning, and
traversal like previous adaptive models. In our domain decomposition approach
for representing large-scale data, we train an set of APMGSRNs in parallel on
separate bricks of the volume to reduce training time while avoiding overhead
necessary for an out-of-core solution for volumes too large to fit in GPU
memory. After training, the lightweight SRNs are used for realtime neural
volume rendering in our open-source renderer, where arbitrary view angles and
transfer functions can be explored. A copy of this paper, all code, all models
used in our experiments, and all supplemental materials and videos are
available at this https URL

提出了一种自适应放置的多网格场景表示网络（APMGSRN），通过域分解训练和推断技术以及多 GPU 系统上的加速并行训练，改善了科学数据的重建精度和网络资源分配。

大规模数据可视化的自适应多网格场景表示网络

Adaptively Placed Multi-Grid Scene Representation Networks for  Large-Scale Data Visualization

6-DoF pose estimation is an essential component of robotic manipulation
pipelines. However, it usually suffers from a lack of generalization to new
instances and object types. Most widely used methods learn to infer the object
pose in a discriminative setup where the model filters useful information to
infer the exact pose of the object. While such methods offer accurate poses,
the model does not store enough information to generalize to new objects. In
this work, we address the generalization capability of pose estimation using
models that contain enough information about the object to render it in
different poses. We follow the line of work that inverts neural renderers to
infer the pose. We propose i-$\sigma$SRN to maximize the information flowing
from the input pose to the rendered scene and invert them to infer the pose
given an input image. Specifically, we extend Scene Representation Networks
(SRNs) by incorporating a separate network for density estimation and introduce
a new way of obtaining a weighted scene representation. We investigate several
ways of initial pose estimates and losses for the neural renderer. Our final
evaluation shows a significant improvement in inference performance and speed
compared to existing approaches.

本文提出了一种新的 6-DoF 姿态估计模型 i-σSRN，通过在神经渲染器中，将目标位置与渲染场景信息联系起来，从而实现对于不同目标的姿态估计和渲染，显著提高了推理性能和速度。

使用隐式场景表示的通用姿态估计

Generalizable Pose Estimation Using Implicit Scene Representations

We present dynamic neural radiance fields for modeling the appearance and
dynamics of a human face. Digitally modeling and reconstructing a talking human
is a key building-block for a variety of applications. Especially, for
telepresence applications in AR or VR, a faithful reproduction of the
appearance including novel viewpoints or head-poses is required. In contrast to
state-of-the-art approaches that model the geometry and material properties
explicitly, or are purely image-based, we introduce an implicit representation
of the head based on scene representation networks. To handle the dynamics of
the face, we combine our scene representation network with a low-dimensional
morphable model which provides explicit control over pose and expressions. We
use volumetric rendering to generate images from this hybrid representation and
demonstrate that such a dynamic neural scene representation can be learned from
monocular input data only, without the need of a specialized capture setup. In
our experiments, we show that this learned volumetric representation allows for
photo-realistic image generation that surpasses the quality of state-of-the-art
video-based reenactment methods.

使用动态神经辐射场模型重新构建并生成能够通过单目输入数据学习的场景表示，结合场景表示网络和低维可变形模型进行虚拟场景生成。