Generating large-scale 3D scenes cannot simply apply existing 3D object
synthesis technique since 3D scenes usually hold complex spatial configurations
and consist of a number of objects at varying scales. We thus propose a
practical and efficient 3D representation that incorporates an equivariant
radiance field with the guidance of a bird's-eye view (BEV) map. Concretely,
objects of synthesized 3D scenes could be easily manipulated through steering
the corresponding BEV maps. Moreover, by adequately incorporating positional
encoding and low-pass filters into the generator, the representation becomes
equivariant to the given BEV map. Such equivariance allows us to produce
large-scale, even infinite-scale, 3D scenes via synthesizing local scenes and
then stitching them with smooth consistency. Extensive experiments on 3D scene
datasets demonstrate the effectiveness of our approach. Our project website is
at this https URL

生成大规模 3D 场景需要应用现有的 3D 对象合成技术，并且场景通常具有复杂的空间配置，并由多个对象组成，不同细度的尺度。因此，我们提出了一种实用且高效的 3D 表示方法，该方法将等变辐射场与鸟瞰图进行了结合。具体来说，通过操纵相应的鸟瞰图，可以轻松操作合成的 3D 场景中的对象。此外，通过充分融合位置编码和低通滤波器到生成器中，该表示对给定的鸟瞰图是等变的。这种等变性使得我们能够通过合成局部场景然后以平滑的一致性连接它们来生成大规模甚至是无限规模的 3D 场景。对 3D 场景数据集的大量实验证明了我们方法的有效性。详见项目网站链接。

BerfScene: 基于 Bev 的等变辐射场用于无限 3D 场景生成

BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D  Scene Generation

This paper presents an example-driven synthesis technique for automating a
large class of data preparation tasks that arise in data science. Given a set
of input tables and an out- put table, our approach synthesizes a table
transformation program that performs the desired task. Our approach is not
restricted to a fixed set of DSL constructs and can synthesize programs from an
arbitrary set of components, including higher-order combinators. At a
high-level, our approach performs type-directed enumerative search over partial
pro- grams but incorporates two key innovations that allow it to scale: First,
our technique can utilize any first-order specification of the components and
uses SMT-based deduction to reject partial programs. Second, our algorithm uses
partial evaluation to increase the power of deduction and drive enumerative
search. We have evaluated our synthesis algorithm on dozens of data preparation
tasks obtained from on-line forums, and we show that our approach can
automatically solve a large class of problems encountered by R users.

该论文提出了一个基于样例的综合技术，能够自动化数据科学中出现的大量数据准备任务，我们的方法不仅限于固定的 DSL 结构，而且还可从任意组件集合中综合程序。论文通过部分程序进行类型导向的枚举搜索，结合两种关键创新技术，即利用任何一阶构建的第一个规范来拒绝部分程序的 SMT 推理，以及利用部分评估来增加推理的能力并推动枚举搜索。我们对数十个从在线论坛中获得的数据准备任务进行了综合算法评估，结果表明我们的方法可以自动解决 R 用户遇到的大类问题。