In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature that enables the incorporation of 3D prior.
Specifically, our method adopts a progressive optimization strategy, which
includes a geometry optimization stage and an appearance refinement stage. In
geometry optimization, a coarse representation is established under a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich details. In this stage, we increase the number
of Gaussians by compactness-based densification to enhance continuity and
improve fidelity. With these designs, our approach can generate 3D content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at this https URL Our code
is available at this https URL

利用高斯雨滴喷洒技术的文本到 3D 生成（GSGEN）方法可以生成具有精细细节和更准确几何形状的高质量 3D 对象。

高斯点云 3D 建模

Text-to-3D using Gaussian Splatting

This paper proposes a fully-automatic, text-guided generative method for
producing periodic, repeating, tile-able 2D art, such as the one seen on
floors, mosaics, ceramics, and the work of M.C. Escher. In contrast to the
standard concept of a seamless texture, i.e., square images that are seamless
when tiled, our method generates non-square tilings which comprise solely of
repeating copies of the same object. It achieves this by optimizing both
geometry and color of a 2D mesh, in order to generate a non-square tile in the
shape and appearance of the desired object, with close to no additional
background details. We enable geometric optimization of tilings by our key
technical contribution: an unconstrained, differentiable parameterization of
the space of all possible tileable shapes for a given symmetry group. Namely,
we prove that modifying the laplacian used in a 2D mesh-mapping technique -
Orbifold Tutte Embedding - can achieve all possible tiling configurations for a
chosen planar symmetry group. We thus consider both the mesh's tile-shape and
its texture as optimizable parameters, rendering the textured mesh via a
differentiable renderer. We leverage a trained image diffusion model to define
a loss on the resulting image, thereby updating the mesh's parameters based on
its appearance matching the text prompt. We show our method is able to produce
plausible, appealing results, with non-trivial tiles, for a variety of
different periodic tiling patterns.

通过优化几何和颜色的二维网格，本文提出了一种全自动的文本引导生成方法，用于生成周期性、可重复、可平铺的二维艺术瓦片，能够产生多种不同的周期平铺图案。

生成艾舍网格

Generative Escher Meshes

In this work, geometry optimization of mechanical truss using computer-aided
finite element analysis is presented. The shape of the truss is a dominant
factor in determining the capacity of load it can bear. At a given parameter
space, our goal is to find the parameters of a hull that maximize the
load-bearing capacity and also don't yield to the induced stress. We rely on
finite element analysis, which is a computationally costly design analysis tool
for design evaluation. For such expensive to-evaluate functions, we chose
Bayesian optimization as our optimization framework which has empirically
proven sample efficient than other simulation-based optimization methods.
By utilizing Bayesian optimization algorithms, the truss design involves
iteratively evaluating a set of candidate truss designs and updating a
probabilistic model of the design space based on the results. The model is used
to predict the performance of each candidate design, and the next candidate
design is selected based on the prediction and an acquisition function that
balances exploration and exploitation of the design space. Our result can be
used as a baseline for future study on AI-based optimization in expensive
engineering domains especially in finite element Analysis.

本文提出了使用计算机辅助有限元分析进行机械桁架的几何优化，利用贝叶斯优化算法使桁架的形状在不产生应力的情况下，最大化其承载能力，以提供基于人工智能的优化的研究方法。

使用贝叶斯优化进行桁架设计优化

Optimization for truss design using Bayesian optimization

We introduce a novel method to obtain high-quality 3D reconstructions from
consumer RGB-D sensors. Our core idea is to simultaneously optimize for
geometry encoded in a signed distance field (SDF), textures from
automatically-selected keyframes, and their camera poses along with material
and scene lighting. To this end, we propose a joint surface reconstruction
approach that is based on Shape-from-Shading (SfS) techniques and utilizes the
estimation of spatially-varying spherical harmonics (SVSH) from subvolumes of
the reconstructed scene. Through extensive examples and evaluations, we
demonstrate that our method dramatically increases the level of detail in the
reconstructed scene geometry and contributes highly to consistent surface
texture recovery.

本文提出一种新的方法，可同时优化几何编码、纹理以及材料和场景照明，以从消费级 RGB-D 传感器中获得高质量的 3D 重建。

Intrinsic3D： 高质量的 3D 重建，通过联合外观和几何优化，带有空间变化照明

Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and  Geometry Optimization with Spatially-Varying Lighting

We describe a global optimization technique using `basin-hopping' in which
the potential energy surface is transformed into a collection of
interpenetrating staircases. This method has been designed to exploit the
features which recent work suggests must be present in an energy landscape for
efficient relaxation to the global minimum. The transformation associates any
point in configuration space with the local minimum obtained by a geometry
optimization started from that point, effectively removing transition state
regions from the problem. However, unlike other methods based upon hypersurface
deformation, this transformation does not change the global minimum. The lowest
known structures are located for all Lennard-Jones clusters up to 110 atoms,
including a number that have never been found before in unbiased searches.

采用 “盆地跳跃” 全局优化技术进行全局最优的李纳 - 琼斯团簇搜索，并成功找到了 110 个原子以内的最优构型。