Powerful priors allow us to perform inference with insufficient information.
In this paper, we propose an autoregressive prior for 3D shapes to solve
multimodal 3D tasks such as shape completion, reconstruction, and generation.
We model the distribution over 3D shapes as a non-sequential autoregressive
distribution over a discretized, low-dimensional, symbolic grid-like latent
representation of 3D shapes. This enables us to represent distributions over 3D
shapes conditioned on information from an arbitrary set of spatially anchored
query locations and thus perform shape completion in such arbitrary settings
(e.g., generating a complete chair given only a view of the back leg). We also
show that the learned autoregressive prior can be leveraged for conditional
tasks such as single-view reconstruction and language-based generation. This is
achieved by learning task-specific naive conditionals which can be approximated
by light-weight models trained on minimal paired data. We validate the
effectiveness of the proposed method using both quantitative and qualitative
evaluation and show that the proposed method outperforms the specialized
state-of-the-art methods trained for individual tasks. The project page with
code and video visualizations can be found at
this https URL

该研究提出了一种基于自回归先验的方法来解决三维形状完形、重建和生成等多模态三维任务，该方法可以在一个任意集合的空间查询位置上对三维形状建模，从而在不同的条件下执行形状补齐。

AutoSDF: 用于 3D 完善、重建和生成的形状先验

AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation

In this paper we present a a deep generative model for lossy video
compression. We employ a model that consists of a 3D autoencoder with a
discrete latent space and an autoregressive prior used for entropy coding. Both
autoencoder and prior are trained jointly to minimize a rate-distortion loss,
which is closely related to the ELBO used in variational autoencoders. Despite
its simplicity, we find that our method outperforms the state-of-the-art
learned video compression networks based on motion compensation or
interpolation. We systematically evaluate various design choices, such as the
use of frame-based or spatio-temporal autoencoders, and the type of
autoregressive prior. In addition, we present three extensions of the basic
method that demonstrate the benefits over classical approaches to compression.
First, we introduce semantic compression, where the model is trained to
allocate more bits to objects of interest. Second, we study adaptive
compression, where the model is adapted to a domain with limited variability,
e.g., videos taken from an autonomous car, to achieve superior compression on
that domain. Finally, we introduce multimodal compression, where we demonstrate
the effectiveness of our model in joint compression of multiple modalities
captured by non-standard imaging sensors, such as quad cameras. We believe that
this opens up novel video compression applications, which have not been
feasible with classical codecs.

本文提出了一种基于深度生成模型的视频压缩方法，采用三维自编码器和自回归先验以最小化速率失真损失，实现编码效果优于现有的基于运动补偿或插值的视频压缩网络，并且在设计选择和压缩技术方面进行了系统评估和扩展，包括语义压缩、自适应压缩和多模态压缩，以便在非标准成像传感器捕获的多个模态数据上实现联合压缩。