This paper introduces CADgpt, an innovative plugin integrating Natural
Language Processing (NLP) with Rhino3D for enhancing 3D modelling in
computer-aided design (CAD) environments. Leveraging OpenAI's GPT-4, CADgpt
simplifies the CAD interface, enabling users, particularly beginners, to
perform complex 3D modelling tasks through intuitive natural language commands.
This approach significantly reduces the learning curve associated with
traditional CAD software, fostering a more inclusive and engaging educational
environment. The paper discusses CADgpt's technical architecture, including its
integration within Rhino3D and the adaptation of GPT-4 capabilities for CAD
tasks. It presents case studies demonstrating CADgpt's efficacy in various
design scenarios, highlighting its potential to democratise design education by
making sophisticated design tools accessible to a broader range of students.
The discussion further explores CADgpt's implications for pedagogy and
curriculum development, emphasising its role in enhancing creative exploration
and conceptual thinking in design education.
Keywords: Natural Language Processing, Computer-Aided Design, 3D Modelling,
Design Automation, Design Education, Architectural Education

CADgpt 是一种将自然语言处理（NLP）与 Rhino3D 集成的创新插件，旨在增强计算机辅助设计（CAD）环境中的 3D 建模。借助 OpenAI 的 GPT-4，CADgpt 通过直观的自然语言指令简化 CAD 界面，使用户，特别是初学者，能够执行复杂的 3D 建模任务。这种方法显著降低了传统 CAD 软件的学习曲线，促进了更具包容性和吸引力的教育环境。

CADgpt: 利用自然语言处理增强计算机辅助设计工作流程的 3D 建模

CADgpt: Harnessing Natural Language Processing for 3D Modelling to  Enhance Computer-Aided Design Workflows

Neural Radiance Field (NeRF) research has attracted significant attention
recently, with 3D modelling, virtual/augmented reality, and visual effects
driving its application. While current NeRF implementations can produce high
quality visual results, there is a conspicuous lack of reliable methods for
evaluating them. Conventional image quality assessment methods and analytical
metrics (e.g. PSNR, SSIM, LPIPS etc.) only provide approximate indicators of
performance since they generalise the ability of the entire NeRF pipeline.
Hence, in this paper, we propose a new test framework which isolates the neural
rendering network from the NeRF pipeline and then performs a parametric
evaluation by training and evaluating the NeRF on an explicit radiance field
representation. We also introduce a configurable approach for generating
representations specifically for evaluation purposes. This employs ray-casting
to transform mesh models into explicit NeRF samples, as well as to "shade"
these representations. Combining these two approaches, we demonstrate how
different "tasks" (scenes with different visual effects or learning strategies)
and types of networks (NeRFs and depth-wise implicit neural representations
(INRs)) can be evaluated within this framework. Additionally, we propose a
novel metric to measure task complexity of the framework which accounts for the
visual parameters and the distribution of the spatial data. Our approach offers
the potential to create a comparative objective evaluation framework for NeRF
methods.

本文介绍了一个新的测试框架以专门用于评估神经辐射场（NeRF）模型的性能，该框架通过训练和评估 NeRF 的显式辐射场表示来解耦神经渲染网络和 NeRF 管道，并提出了一种配置方法来生成特定于评估目的的表示形式。我们还提出了一种新的指标来衡量该框架的任务复杂性，该指标考虑到了视觉参数和空间数据的分布。

朝着稳健的 NeRF 评估框架

Towards a Robust Framework for NeRF Evaluation

The ability of neural radiance fields or NeRFs to conduct accurate 3D
modelling has motivated application of the technique to scene representation.
Previous approaches have mainly followed a centralised learning paradigm, which
assumes that all training images are available on one compute node for
training. In this paper, we consider training NeRFs in a federated manner,
whereby multiple compute nodes, each having acquired a distinct set of
observations of the overall scene, learn a common NeRF in parallel. This
supports the scenario of cooperatively modelling a scene using multiple agents.
Our contribution is the first federated learning algorithm for NeRF, which
splits the training effort across multiple compute nodes and obviates the need
to pool the images at a central node. A technique based on low-rank
decomposition of NeRF layers is introduced to reduce bandwidth consumption to
transmit the model parameters for aggregation. Transferring compressed models
instead of the raw data also contributes to the privacy of the data collecting
agents.

本文提出了一种 Federated Learning 算法，利用低秩分解技术实现了多计算节点之间共同学习 NeRF，同时解决了数据集隐私的问题。

联邦神经辐射场

Federated Neural Radiance Fields

In this paper, we propose a novel hybrid representation and end-to-end
trainable network architecture to model fully editable and customizable neural
avatars. At the core of our work lies a representation that combines the
modeling power of neural fields with the ease of use and inherent 3D
consistency of skinned meshes. To this end, we construct a trainable feature
codebook to store local geometry and texture features on the vertices of a
deformable body model, thus exploiting its consistent topology under
articulation. This representation is then employed in a generative auto-decoder
architecture that admits fitting to unseen scans and sampling of realistic
avatars with varied appearances and geometries. Furthermore, our representation
allows local editing by swapping local features between 3D assets. To verify
our method for avatar creation and editing, we contribute a new high-quality
dataset, dubbed CustomHumans, for training and evaluation. Our experiments
quantitatively and qualitatively show that our method generates diverse
detailed avatars and achieves better model fitting performance compared to
state-of-the-art methods. Our code and dataset are available at
this https URL

本文提出一种新的混合表征和端到端可训练的网络体系结构，以建模完全可编辑和可定制的神经化身，并设计实现一种特征码本以存储可变形体模型的本地几何和纹理特征。该模型具有 3D 一致性，并利用神经场的模建模力量。此外，该方法还可在三维资产之间交换本地特征以实现局部编辑。通过贡献一个新的高质量数据集，该方法生成了多样化的详细化身，并比现有方法获得更好的模型拟合性能。