Recently, 3D Gaussian splatting (3D-GS) has achieved great success in
reconstructing and rendering real-world scenes. To transfer the high rendering
quality to generation tasks, a series of research works attempt to generate
3D-Gaussian assets from text. However, the generated assets have not achieved
the same quality as those in reconstruction tasks. We observe that Gaussians
tend to grow without control as the generation process may cause indeterminacy.
Aiming at highly enhancing the generation quality, we propose a novel framework
named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable
geometry, which evolves over the whole generation process. Along different
stages of our framework, both the geometry and appearance can be enriched
progressively. The final output asset is constructed with 3D Gaussians bound to
mesh, which shows significantly enhanced details and quality compared with
previous methods. Notably, the generated asset can also be seamlessly
integrated into downstream manipulation pipelines, e.g. animation, composition,
and simulation etc., greatly promoting its potential in wide applications.
Demos are available at this https URL

提出了一种名为 GaussianDreamerPro 的新框架，通过将高斯函数与合理几何形状相结合，逐步丰富几何与外观，构建了与以前方法相比具有显着改进细节和质量的 3D 高斯函数绑定网格的生成资产，可无缝集成于下游操作流水线，大大扩展了其在广泛应用中的潜力。

GaussianDreamerPro：具有极高质量的可操作三维高斯文本

GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly  Enhanced Quality

3D city generation with NeRF-based methods shows promising generation results
but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has
emerged as a highly efficient alternative for object-level 3D generation.
However, adapting 3D-GS from finite-scale 3D objects and humans to
infinite-scale 3D cities is non-trivial. Unbounded 3D city generation entails
significant storage overhead (out-of-memory issues), arising from the need to
expand points to billions, often demanding hundreds of Gigabytes of VRAM for a
city scene spanning 10km^2. In this paper, we propose GaussianCity, a
generative Gaussian Splatting framework dedicated to efficiently synthesizing
unbounded 3D cities with a single feed-forward pass. Our key insights are
two-fold: 1) Compact 3D Scene Representation: We introduce BEV-Point as a
highly compact intermediate representation, ensuring that the growth in VRAM
usage for unbounded scenes remains constant, thus enabling unbounded city
generation. 2) Spatial-aware Gaussian Attribute Decoder: We present
spatial-aware BEV-Point decoder to produce 3D Gaussian attributes, which
leverages Point Serializer to integrate the structural and contextual
characteristics of BEV points. Extensive experiments demonstrate that
GaussianCity achieves state-of-the-art results in both drone-view and
street-view 3D city generation. Notably, compared to CityDreamer, GaussianCity
exhibits superior performance with a speedup of 60 times (10.72 FPS v.s. 0.18
FPS).

高效合成无边界 3D 城市的 GaussianCity 系统，采用 3D 城市生成的 NeRF 和 3D-Gauusian Splatting 方法，通过引入 BEV-Point 作为紧凑的中间表示和空间感知的 BEV-Point 解码器，实现了优于 CityDreamer 的最先进的无人机视角和街景视角下的 3D 城市生成性能提升 60 倍（10.72 FPS 对 0.18 FPS）。

GaussianCity：基于高斯点绘制的无界三维城市生成

GaussianCity: Generative Gaussian Splatting for Unbounded 3D City  Generation

Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize
3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR
data capabilities but also overlooks the potential advantages of fusing LiDAR
with camera data. In this paper, we design a novel tightly coupled LiDAR-Camera
Gaussian Splatting (TCLC-GS) to fully leverage the combined strengths of both
LiDAR and camera sensors, enabling rapid, high-quality 3D reconstruction and
novel view RGB/depth synthesis. TCLC-GS designs a hybrid explicit (colorized 3D
mesh) and implicit (hierarchical octree feature) 3D representation derived from
LiDAR-camera data, to enrich the properties of 3D Gaussians for splatting. 3D
Gaussian's properties are not only initialized in alignment with the 3D mesh
which provides more completed 3D shape and color information, but are also
endowed with broader contextual information through retrieved octree implicit
features. During the Gaussian Splatting optimization process, the 3D mesh
offers dense depth information as supervision, which enhances the training
process by learning of a robust geometry. Comprehensive evaluations conducted
on the Waymo Open Dataset and nuScenes Dataset validate our method's
state-of-the-art (SOTA) performance. Utilizing a single NVIDIA RTX 3090 Ti, our
method demonstrates fast training and achieves real-time RGB and depth
rendering at 90 FPS in resolution of 1920x1280 (Waymo), and 120 FPS in
resolution of 1600x900 (nuScenes) in urban scenarios.

设计一种新的紧密耦合的 LiDAR-Camera 高斯扩散 (TCLC-GS) 方法，充分利用 LiDAR 和相机传感器的优势，实现快速高质量的 3D 重建和新视角 RGB / 深度合成，通过富含上下文信息的 3D 网格和隐式特征来丰富 3D 高斯扩散的属性和优化过程。