Tootfinder

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Hangyu Ji
https://arxiv.org/abs/2506.05297 https://

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Accurate 3D medical image segmentation demands architectures capable of reconciling global context modeling with spatial topology preservation. While State Space Models (SSMs) like Mamba show potential for sequence modeling, existing medical SSMs suffer from encoder-decoder incompatibility: the encoder's 1D sequence flattening compromises spatial structures, while conventional decoders fail to leverage Mamba's state propagation. We present DM-SegNet, a Dual-Mamba architecture integrating direct…

Coupling 1D stellar evolution with 3D-hydrodynamical simulations on-the-fly III: stellar evolution at different metallicities
Yixiao Zhou, Jakob L. R{\o}rsted, Achim Weiss, Andreas C. S. J{\o}rgensen, Cis Lagae, Luisa F. Rodr\'iguez D\'iaz, Yaguang Li, Mark L. Winther, Jens R. Larsen, J{\o}rgen Christensen-Dalsgaard, Oleg Kochukhov, Karen R. Pollard, Tanda Li

Coupling 1D stellar evolution with 3D-hydrodynamical simulations on-the-fly III: stellar evolution at different metallicities
A major weakness in one-dimensional (1D) stellar structure and evolution modeling is the simplified treatment of convection, which leads to erroneous near-surface stratification and considerable uncertainties in predicted effective temperatures and luminosities of low-mass stars. In a series of preceding works, a novel method for coupling 1D stellar structural models with a grid of 3D surface convection simulations during stellar evolution was developed, at solar metallicity. This 1D-3D couplin…

This https://arxiv.org/abs/2504.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling
Realistic digital garment modeling remains a labor-intensive task due to the intricate process of translating 2D sewing patterns into high-fidelity, simulation-ready 3D garments. We introduce GarmageNet, a unified generative framework that automates the creation of 2D sewing patterns, the construction of sewing relationships, and the synthesis of 3D garment initializations compatible with physics-based simulation. Central to our approach is Garmage, a novel garment representation that encodes e…

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang, Huiyu Yang, Jianchun Wang
https://arxiv.org/abs/2506.04898

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the …

This https://arxiv.org/abs/2505.07286 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major bottleneck lies in the twisted probability path of multi-modalities -- continuous 3D positions and discrete 2D topologies -- which jointly determine molecular geometries. By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB-Optimal Sche…

This https://arxiv.org/abs/2501.14147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction with data streamed from multiple robots and devices remains a challenge. To that end, we propose HAMMER, a server-based collaborative Gaussian Splatting method that leverages widely available ROS communication infrastructure to generate 3D, metric-semantic maps from asynchronous robot data-streams with no prior knowl…

This https://arxiv.org/abs/2412.04339 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction
Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing…

This https://arxiv.org/abs/2405.14979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding t…

FlexPainter: Flexible and Multi-View Consistent Texture Generation
Dongyu Yan, Leyi Wu, Jiantao Lin, Luozhou Wang, Tianshuo Xu, Zhifei Chen, Zhen Yang, Lie Xu, Shunsi Zhang, Yingcong Chen
https://arxiv.org/abs/2506.02620

FlexPainter: Flexible and Multi-View Consistent Texture Generation
Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt modalities may prevent creators from producing desired results. Furthermore, inconsistencies between generated multi-view images often lead to poor texture generation quality. To address these issues, we introduce \textbf{FlexPainter}, a novel texture generation …

A validated coupled three-dimensional hydrodynamic and spectral wind-wave model for the western north Atlantic Ocean
Maria Venolia, Reza Marsooli, Jaime R. Calzada
https://arxiv.org/abs/2505.18803

A validated coupled three-dimensional hydrodynamic and spectral wind-wave model for the western north Atlantic Ocean
Wind-wave and ocean current interactions affect critical coastal and oceanic processes, yet modeling these interactions presents significant challenges. The western North Atlantic Ocean provides an ideal test environment for coupled hydrodynamics and wind wave models, thanks to its energetic surface currents such as the Gulf Stream. This study evaluates a high-resolution coupled SCHISM WWM III model, utilizing NOAA's 'STOFS-3D-Atlantic' computational mesh, while incorporating three-dimensional …

This https://arxiv.org/abs/2405.14979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding t…

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Weihang Liu, Yuhui Zhong, Yuke Li, Xi Chen, Jiadi Cui, Honglong Zhang, Lan Xu, Xin Lou, Yujiao Shi, Jingyi Yu, Yingliang Zhang
https://arxiv.org/abs/2505.21041

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain li…

This https://arxiv.org/abs/2505.21041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain li…