论文检索

Felix Windisch, Thomas Köhler 0006, Lukas Radl, Mattia D'Urso, Michael Steiner 0011, Dieter Schmalstieg, Markus Steinberger

Gaussian Splatting has emerged as a high-performance technique for novel view synthesis, enabling real-time rendering and high-quality reconstruction of small scenes. However, scaling to larger environments has so far relied on partitioning the scene into chunks—a strategy that introduces artifacts at chunk boundaries, complicates training across varying scales, and is poorly suited to unstructured scenarios such as city-scale flyovers combined with street-level views. Moreover, rendering remains fundamentally limited by GPU memory, as all visible chunks must reside in VRAM simultaneously. We introduce A LoD of Gaussians, a framework for training and rendering ultra-large-scale Gaussian scenes on a single consumer-grade GPU without partitioning. Our method stores the full scene out-of-core (i.e., in CPU memory) and trains a Level-of-Detail (LoD) representation directly, dynamically streaming only the relevant Gaussians. A hybrid data structure combining Gaussian hierarchies with Sequential Point Trees enables efficient, view-dependent LoD selection, while a lightweight caching and view scheduling system exploits temporal coherence to minimize the loading overhead. Together, these innovations enable seamless multi-scale reconstruction and interactive visualization of complex scenes—from broad aerial views to fine-grained ground-level details. Supplementary materials and code are available on our project page: https://felixwindisch.github.io/ALoDOfGaussians.

Junran Ding, Weihang Liu, Yuke Li, Yizhou Wang, Antong Li, Qihan Ding, Xin Lou 0001, Jingyi Yu 0001

We present SHARP-GS, a high-performance framework that unlocks real-time 8K rendering for immersive virtual experiences by overcoming the scaling bottlenecks of 3D Gaussian Splatting (3DGS). While 3DGS excels at real-time view synthesis, its performance degrades non-linearly at ultra-high resolutions due to excessive binning overhead, uncoalesced memory access, and redundant per-pixel arithmetic. SHARP-GS addresses these inefficiencies through three key contributions: (i) Resolution-Aware Adaptive Binning, which maintains a constant overlap factor via dynamic tile sizing and employs fine-grained sub-tile culling; (ii) a Morton-Ordered Memory Layout to ensure spatially coherent memory access; and (iii) Forward Differencing, which replaces expensive probability density function evaluations with efficient incremental updates. On high-end consumer GPUs, our framework achieves an average 2.55× speedup at 8K resolution, sustaining over 250 FPS with negligible visual quality loss. This performance delivers the necessary pixel throughput to enable high-fidelity, 120 Hz stereoscopic VR experiences. Code and data for this paper are at https://github.com/dingjr7/SHARP-GS.

Linjie Lyu, Ayush Tewari, Jianchun Chen, Thomas Leimkühler, Christian Theobalt

3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control relies on screen-space positional gradients, which do not distinguish between geometric misplacement and frequency aliasing, often leading to either over-blurred high-frequency textures or inefficient over-densification. We present a structure-aware densification framework. Our key insight is that the decision to subdivide a Gaussian should be driven by an explicit comparison between its projected screen-space extent and the local structure of the texture it seeks to represent. We introduce a multi-scale frequency analysis combining structure tensors with Laplacian scale space analysis to estimate the dominant frequency at each pixel, enabling robust supervision across varying texture scales. Based on this analysis, we define η , a per-Gaussian, per-axis frequency violation metric that indicates when a primitive may be under-resolving local texture details. Unlike methods that perform isotropic splitting (e.g., splitting each Gaussian into two smaller ones with uniform shape), our approach performs anisotropic splitting. For each axis with high η , we compute a split factor to better resolve the local frequency content. We further introduce a multiview consistency criterion that aggregates η observations across multiple views. By performing densification early and faster, we skip the lengthy iterative densification phases required by baseline methods and achieve significantly faster convergence. Experiments on standard benchmarks demonstrate that our method also achieves superior reconstruction quality, particularly in high-frequency regions.

Yunfan Zeng, Li Ma, Pedro V. Sander

We present a framework for synthesizing large, richly detailed scenes from multiple Gaussian Splatting exemplars using a class-aware Wang tiling formulation, building on Gaussian Splatting Wang Tiles as the underlying representation. The method introduces a multi-class representation that supports both hard class regions and hybrid tiles that mix Gaussians from different classes at fine spatial scales, enabling smooth and organic transitions between classes. Hybrid tiles are generated at runtime by a priority-based selection scheme that selects and blends tile contents based on class importance and spatial context. To preserve real-time performance in large multi-class scenes, we introduce a CPU–GPU hybrid sorting and caching strategy that balances workload and improves frame stability. The system also includes a new interactive authoring tool for real-time editing of scene structure and class distributions. Finally, we demonstrate that the approach can be applied to arbitrary meshes with polycube mapping, turning the tiled Gaussian fields into high-frequency 3D textures with controllable, artist-directed variation, providing rich visuals and great flexibility.

Tobias Djuren, Ugo Paavo Finnendahl, Markus Worchel, Hendrik Meyer, Marc Alexa

Different shape representations facilitate different computations. Surface representations, in particular meshes, are often used for modeling, whereas volume representations are useful for spatial queries such as intersection or containment. Optimizing a surface representation based on a volumetric properties by gradient descent requires the derivatives of the volume relative to its bounding surface. We derive this gradient for winding numbers and show that it can be efficiently computed for volumetric values sampled on a regular grid (voxel representation) and surface parameters based on vertex sets (triangle meshes). This enables an efficient solution for a variety of optimization problems. We demonstrate the practical use of this approach at the examples of deforming meshes to resolve intersections, being manufacturable by cutting with a bandsaw from three directions, and creating shapes that are close to tiling 3D space.

Suzuran Takikawa, Leo Foord-Kelcey, Oliver Oxford, Nicholas Vining, Alla Sheffer

该来源暂未提供摘要。

Yang Liu 0239, Daxuan Ren, Yijie Ding, Jianmin Zheng, Fang Deng

Advances in large language models (LLMs) have sparked interest in automating parametric CAD modeling through natural language. Existing LLM-based approaches often treat CAD modeling as flat text generation, overlooking the hierarchical structure and geometric constraints inherent in CAD programs. We present CAD-Factory, a Text-to-CAD generation system for language-driven CAD modeling that explicitly models the structural and parametric semantics of CAD programs. Our core contribution is a new formulation of CAD generation as structured program synthesis, coupled with a learnable hierarchical CAD program representation that disentangles structural topology from parameter instantiation. Building on this formulation, CAD-Factory adopts a manager–programmer–reviewer architecture: a planning agent infers program hierarchy, a coding agent instantiates symbolic and numerical parameters, and an evaluation module enforces structural validity and geometric feasibility, which supports structure-aware reasoning, constraint-consistent generation, and interpretable program synthesis. An editor–viewer loop further enables part-aware code refinement through visual feedback, supporting iterative and controllable design workflows. We also contribute a process-oriented annotation pipeline and a Text–CAD dataset with parameter-free or partially specified descriptions that reflect real-world design expression. Extensive experiments demonstrate that CAD-Factory significantly improves structural correctness and geometric consistency across diverse CAD code generation and editing tasks, establishing a structured, interpretable, and robust framework for AI-assisted design.

Shiyu Tan, Zixuan Zhao, Hao Gao, Zhiheng Chen, Xiaolong Yin, Enya Shen

Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images remains challenging due to the complexity of topological constraints and operation sequences. We present Img2CADSeq, a multi-stage pipeline that overcomes these limitations by encoding CAD sequences into a three-level hierarchical codebook. Guided by an importance prioritization, this strategy values profiles over details, compressing long sequences into a stable discrete latent space. To bridge the modality gap, we leverage a coarse-to-fine point cloud intermediate, aligning 2D visual features with 3D CAD sequences via contrastive learning to condition a VQ-Diffusion model. Supported by newly introduced CAD-220K and PrintCAD datasets, our approach ensures robust industrial domain adaptation. Extensive experiments demonstrate that Img2CADSeq significantly outperforms state-of-the-art methods, producing standard STEP files that can be directly used in commercial CAD software. Code and data for this paper are at https://github.com/Rilpraa0110/Img2CADSeq

Jing Li 0113, Yihang Fu, Falai Chen

该来源暂未提供摘要。

Junran Lu, Yuanqi Li, Hengji Li, Jie Guo 0001, Yanwen Guo 0001

该来源暂未提供摘要。

Yilin Liu, Niladri Shekhar Dutt, Changjian Li 0001, Niloy J. Mitra

Computer-Aided Design (CAD) models, given their compactness and precision, remain the industry standard for designing and fabricating engineering objects. However, language-guided CAD editing is still in its infancy, largely due to missing semantic connection between user commands and underlying shape geometry, a problem exacerbated by the shortage of paired text-and-edit CAD datasets. While recent Multimodal Large Language Models (mLLMs) have attempted to bridge this gap, their reliance on CAD construction history –often an expensive and hard to obtain input– severely limits their expressiveness and restricts their usage. We present B-repLer, a novel framework that directly connects natural language with editing CAD models by operating in a learned latent space. Importantly, our approach bypasses the need for construction history, enabling semantic edits on a wide range of geometries, from simple prismatic parts to complex freeform shapes defined by B-Spline surfaces. To facilitate this research, we introduce BrepEDIT-240K, the first large-scale dataset for this task. We demonstrate how this paired dataset can be automatically generated, (user) validated, and scaled by leveraging existing CAD tools, in conjunction with mLLMs, to create the required paired data without relying on any external annotations. Our results demonstrate that B-repLer can accurately perform complex edits on complex CAD shapes, even when the input edit specifications are high-level and ambiguous to interpret, consistently producing valid, high-quality CAD outputs enabling a class of text-guided edits not previously possible. Project page is at https://yilinliu77.github.io/brepler.github.io/.

Dafei Qin, Rui Xu 0016, Zeyu Shen 0002, Kaichun Qiao, Hongyang Lin, Qixuan Zhang, Huaijin Pi, Lan Xu 0003, Jingyi Yu 0001, Wenping Wang 0001 等

Generative CAD modeling has broad design and application potential. Despite significant advances in Boundary Representation (B-Rep) generation, the dominant representation in CAD, existing methods largely depend on uniformly sampled point- or grid-based geometry representations, sacrificing native surface types and parameters and thereby limiting geometric fidelity and downstream usability. We present ParaCAD, an autoregressive framework for point-cloud-conditioned B-Rep generation that directly operates on native parametric surfaces. ParaCAD introduces a surface-centric tokenization that explicitly encodes each face by its exact surface type and continuous parameters, preserving the intrinsic semantics of CAD geometry. Our model first generates parametric surfaces with constrained UV domains, and then constructs a valid B-Rep by globally intersecting these surfaces to recover edges and vertices. ParaCAD places point-cloud-conditioned generation at the core of B-Rep synthesis, making it practical for user-guided reconstruction and seamless integration into existing 3D generation pipelines. Extensive experiments demonstrate that ParaCAD produces accurate B-Reps with faithful point-cloud alignment, outperforming point-based baselines in geometric precision, robustness, watertightness and downstream usability.

Yilin Liu, Pradeep Kumar Jayaraman, Chinthala Reddy, Xiang Xu 0008, Hooman Shayani

Boundary Representation (B-rep) is the most commonly used data format in Computer-Aided Design (CAD) due to its analytical precision and direct support for parametric editing. However, its heterogeneous data structure— continuous parametric geometry with discrete topological graphs—poses fundamental challenges for deep learning models. Existing methods often directly predict the heterogeneous B-rep graph, relying on fixed-size padding or sequential tokenization to handle the varying cardinality of the geometric primitives. These approaches struggle with the combinatorial complexity of CAD models. The discrete, non-differentiable nature of the graph data structure prevents end-to-end optimization of the geometry and watertightness. In this work we introduce DualBrep, a novel continuous representation that unifies B-rep geometry and topology within a fully structured Euclidean domain. DualBrep encodes a CAD model using dual scalar fields: a Signed Distance Function (SDF) to represent the global shape geometry, and an Unsigned Distance Field (UDF) that implicitly encodes the topological structure via a Voronoi partitioning of the surface elements. Rather than processing these fields independently, we compress them into a single latent space. While the dual-field formulation alone already gives reconstruction a flexible, primitive-free segmentation signal that adapts to arbitrary face counts and surface types, the shared latent also makes generation tractable: a Flow Matching model can sample geometry and topology jointly from a single code, avoiding the error accumulation that plagues sequential or autoregressive B-rep predictors. Finally, we use a neural rebuilder to extract explicit B-rep models—comprising both prismatic and free-form primitives—directly from our continuous dual scalar fields. We demonstrate that DualBrep serves as a robust, unified backbone for CAD B-rep learning, achieving strong performance in both reverse engineering from raw point clouds and generative modeling via latent flow matching. Code is available at https://github.com/AutodeskAILab/DualBrep.

Gyujin Sim, Seungjoo Shin, Hosung Jeon, Gwangsoon Lee, Hyon-Gon Choo, Sunghyun Cho

该来源暂未提供摘要。

Inbar Gat, Dana Cohen-Bar, Guy Levy, Elad Richardson, Daniel Cohen-Or

该来源暂未提供摘要。

Etai Sella, Hao Phung, Nitay Amiel, Or Litany, Or Patashnik, Hadar Averbuch-Elor

该来源暂未提供摘要。

Teng-Fang Hsiao, Bo-Kai Ruan, Yu-Lun Liu 0001, Hong-Han Shuai

该来源暂未提供摘要。

Hengyu Meng, Lanjiong Li, Zhijing Shao, Yingda Yin, Lingting Zhu, Zeyu Hu, Xin Wang 0178, Ligang Liu 0001, Zeyu Wang

Inverse sculpting modeling aims to decompose a sculpted mesh into an underlying base shape and reusable geometric details, enabling non-expert users to inherit professional sculpting effort. We present InvSculpt, a novel inverse sculpting framework that decomposes a sculpted mesh into a high-fidelity underlying shape and reusable geometric details represented as a vector displacement field (VDF). Our approach combines semantic priors from text-guided 2D image editing with a 3D rectified flow model to perform inversion-based, mask-free detail removal, recovering an underlying shape that preserves the identity of the source mesh. To represent sculpted details in a lossless and transferable manner, we extract a VDF defined on the surface of the recovered underlying shape and learn a continuous neural representation for geometry-aware transfer. We observe that standard conditional sampling after inversion often suffers from trajectory drift, leading to identity shift and low-frequency distortion. To address this issue, we introduce a trajectory correction strategy that constrains early sampling steps to follow the inversion path, effectively stabilizing subsequent conditional guidance. This design enables robust detail removal and precise extraction of the VDF. Extensive experiments demonstrate that InvSculpt achieves significantly higher-quality mesh decomposition than prior methods and supports a wide range of applications, including geometry redesign and high-fidelity geometric detail transfer.

Dong-Yang Li, Wang Zhao, Yuxin Chen, Wenbo Hu 0002, Meng-Hao Guo, Fang-Lue Zhang, Ying Shan, Shi-Min Hu 0001

该来源暂未提供摘要。

Yiheng Zhu, Kangle Deng, Jean-Philippe Fauconnier, Inaki Navarro, Daiqing Li, Ava Pun, Yinan Zhang, Peiye Zhuang, Xiaoxia Sun, Maneesh Agrawala 等

该来源暂未提供摘要。