In this paper, we showcase the effectiveness of optimizing monocular camera
poses as a continuous function of time. The camera poses are represented using
an implicit neural function which maps the given time to the corresponding
camera pose. The mapped camera poses are then used for the downstream tasks
where joint camera pose optimization is also required. While doing so, the
network parameters -- that implicitly represent camera poses -- are optimized.
We exploit the proposed method in four diverse experimental settings, namely,
(1) NeRF from noisy poses; (2) NeRF from asynchronous Events; (3) Visual
Simultaneous Localization and Mapping (vSLAM); and (4) vSLAM with IMUs. In all
four settings, the proposed method performs significantly better than the
compared baselines and the state-of-the-art methods. Additionally, using the
assumption of continuous motion, changes in pose may actually live in a
manifold that has lower than 6 degrees of freedom (DOF) is also realized. We
call this low DOF motion representation as the \emph{intrinsic motion} and use
the approach in vSLAM settings, showing impressive camera tracking performance.

优化时间连续的单目相机姿态的有效性，采用隐式神经函数表示相机姿态，用于下游任务的联合相机姿态优化，提出的方法在四个实验设置中表现显著优于其他基准和现有方法，同时认识到连续运动中的姿态变化可能存在低于 6 个自由度的流形，称之为内在运动，在 vSLAM 设置中展示出强大的相机追踪性能。

神经隐式表示中的单目相机连续姿态

Continuous Pose for Monocular Cameras in Neural Implicit Representation

Recent works with an implicit neural function shed light on representing
images in arbitrary resolution. However, a standalone multi-layer perceptron
shows limited performance in learning high-frequency components. In this paper,
we propose a Local Texture Estimator (LTE), a dominant-frequency estimator for
natural images, enabling an implicit function to capture fine details while
reconstructing images in a continuous manner. When jointly trained with a deep
super-resolution (SR) architecture, LTE is capable of characterizing image
textures in 2D Fourier space. We show that an LTE-based neural function
achieves favorable performance against existing deep SR methods within an
arbitrary-scale factor. Furthermore, we demonstrate that our implementation
takes the shortest running time compared to previous works.

本文通过引入 Local Texture Estimator (LTE) 的方法，使 implicit function 可以在连续的过程中捕捉细节，在 2D Fourier 空间表征图像纹理，达到了显著的图像超分辨率重建性能，并且在运行时间上比以往的方法更高效。

隐式表示函数的局部纹理估计器

Local Texture Estimator for Implicit Representation Function

Attention-based models, exemplified by the Transformer, can effectively model
long range dependency, but suffer from the quadratic complexity of
self-attention operation, making them difficult to be adopted for
high-resolution image generation based on Generative Adversarial Networks
(GANs). In this paper, we introduce two key ingredients to Transformer to
address this challenge. First, in low-resolution stages of the generative
process, standard global self-attention is replaced with the proposed
multi-axis blocked self-attention which allows efficient mixing of local and
global attention. Second, in high-resolution stages, we drop self-attention
while only keeping multi-layer perceptrons reminiscent of the implicit neural
function. To further improve the performance, we introduce an additional
self-modulation component based on cross-attention. The resulting model,
denoted as HiT, has a nearly linear computational complexity with respect to
the image size and thus directly scales to synthesizing high definition images.
We show in the experiments that the proposed HiT achieves state-of-the-art FID
scores of 30.83 and 2.95 on unconditional ImageNet $128 \times 128$ and FFHQ
$256 \times 256$, respectively, with a reasonable throughput. We believe the
proposed HiT is an important milestone for generators in GANs which are
completely free of convolutions. Our code is made publicly available at
this https URL

本文介绍了将多维块自注意力与多层感知机结合的 Transformer 模型 HiT，该模型在高清图像生成领域的表现优于传统基于卷积的方法。