Tootfinder

Arbitrary-Scale 3D Gaussian Super-Resolution
Huimin Zeng, Yue Bai, Yun Fu
https://arxiv.org/abs/2508.16467 https://arxiv.org/pdf/2508.16467

Arbitrary-Scale 3D Gaussian Super-Resolution
Existing 3D Gaussian Splatting (3DGS) super-resolution methods typically perform high-resolution (HR) rendering of fixed scale factors, making them impractical for resource-limited scenarios. Directly rendering arbitrary-scale HR views with vanilla 3DGS introduces aliasing artifacts due to the lack of scale-aware rendering ability, while adding a post-processing upsampler for 3DGS complicates the framework and reduces rendering efficiency. To tackle these issues, we build an integrated framewor…

PS-GS: Gaussian Splatting for Multi-View Photometric Stereo
Yixiao Chen, Bin Liang, Hanzhi Guo, Yongqing Cheng, Jiayi Zhao, Dongdong Weng
https://arxiv.org/abs/2507.18231 https:…

PS-GS: Gaussian Splatting for Multi-View Photometric Stereo
Integrating inverse rendering with multi-view photometric stereo (MVPS) yields more accurate 3D reconstructions than the inverse rendering approaches that rely on fixed environment illumination. However, efficient inverse rendering with MVPS remains challenging. To fill this gap, we introduce the Gaussian Splatting for Multi-view Photometric Stereo (PS-GS), which efficiently and jointly estimates the geometry, materials, and lighting of the object that is illuminated by diverse directional ligh…

G2S-ICP SLAM: Geometry-aware Gaussian Splatting ICP SLAM
Gyuhyeon Pak, Hae Min Cho, Euntai Kim
https://arxiv.org/abs/2507.18344 https://arxiv.org/pdf/2507.…

G2S-ICP SLAM: Geometry-aware Gaussian Splatting ICP SLAM
In this paper, we present a novel geometry-aware RGB-D Gaussian Splatting SLAM system, named G2S-ICP SLAM. The proposed method performs high-fidelity 3D reconstruction and robust camera pose tracking in real-time by representing each scene element using a Gaussian distribution constrained to the local tangent plane. This effectively models the local surface as a 2D Gaussian disk aligned with the underlying geometry, leading to more consistent depth interpretation across multiple viewpoints comp…

Virtual Memory for 3D Gaussian Splatting
Jonathan Haberl, Philipp Fleck, Clemens Arth
https://arxiv.org/abs/2506.19415 https://arxiv.…

Virtual Memory for 3D Gaussian Splatting
3D Gaussian Splatting represents a breakthrough in the field of novel view synthesis. It establishes Gaussians as core rendering primitives for highly accurate real-world environment reconstruction. Recent advances have drastically increased the size of scenes that can be created. In this work, we present a method for rendering large and complex 3D Gaussian Splatting scenes using virtual memory. By leveraging well-established virtual memory and virtual texturing techniques, our approach efficie…

Random-phase Gaussian Wave Splatting for Computer-generated Holography
Brian Chao, Jacqueline Yang, Suyeon Choi, Manu Gopakumar, Ryota Koiso, Gordon Wetzstein
https://arxiv.org/abs/2508.17480

Random-phase Gaussian Wave Splatting for Computer-generated Holography
Holographic near-eye displays offer ultra-compact form factors for virtual and augmented reality systems, but rely on advanced computer-generated holography (CGH) algorithms to convert 3D scenes into interference patterns that can be displayed on spatial light modulators (SLMs). Gaussian Wave Splatting (GWS) has recently emerged as a powerful CGH paradigm that allows for the conversion of Gaussians, a state-of-the-art neural 3D representation, into holograms. However, GWS assumes smooth-phase d…

GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
SeungJun Moon, Hah Min Lew, Seungeun Lee, Ji-Su Kang, Gyeong-Moon Park
https://arxiv.org/abs/2507.18155 htt…

GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
Despite recent progress in 3D head avatar generation, balancing identity preservation, i.e., reconstruction, with novel poses and expressions, i.e., animation, remains a challenge. Existing methods struggle to adapt Gaussians to varying geometrical deviations across facial regions, resulting in suboptimal quality. To address this, we propose GeoAvatar, a framework for adaptive geometrical Gaussian Splatting. GeoAvatar leverages Adaptive Pre-allocation Stage (APS), an unsupervised method that se…

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Hongbi Zhou, Zhangkai Ni
https://arxiv.org/abs/2506.12400 https://

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training proce…

MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
Hanzhi Chang, Ruijie Zhu, Wenjie Chang, Mulin Yu, Yanzhe Liang, Jiahao Lu, Zhuoyuan Li, Tianzhu Zhang
https://arxiv.org/abs/2508.17811

MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve su…

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Haoxuan Qu, Yujun Cai, Hossein Rahmani, Ajay Kumar, Junsong Yuan, Jun Liu
https://arxiv.org/abs/2507.11321

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel frame…

Generating Human-AI Collaborative Design Sequence for 3D Assets via Differentiable Operation Graph
Xiaoyang Huang, Bingbing Ni, Wenjun Zhang
https://arxiv.org/abs/2508.17645 htt…

Generating Human-AI Collaborative Design Sequence for 3D Assets via Differentiable Operation Graph
The emergence of 3D artificial intelligence-generated content (3D-AIGC) has enabled rapid synthesis of intricate geometries. However, a fundamental disconnect persists between AI-generated content and human-centric design paradigms, rooted in representational incompatibilities: conventional AI frameworks predominantly manipulate meshes or neural representations (\emph{e.g.}, NeRF, Gaussian Splatting), while designers operate within parametric modeling tools. This disconnection diminishes the pr…