subject

cs.GR

cs.GR: explore 104 source-linked works published from 2023 to 2026, with original documents and citations.

This collection is a preview while coverage and quality are evaluated.

Coverage and selection

Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges

This paper surveys the state of transparency rendering in Computer-Aided Design (CAD), with a focus on both practical deployment in industrial systems and the underlying algorithms. We first review current approaches to transparency rendering in CAD environments and outline application scenarios in which accurate and performant transparency is critical for design inspection, communication, and decision-making. We then analyze the trade-offs between approximate and exact transparency techniques, comparing their performance-quality balance on desktop and mobile platforms and discussing criteria for selecting appropriate methods. The survey further identifies the need for robust benchmarks, quality metrics, and evaluation methodologies tailored to CAD-specific visualization tasks. We examine techniques for emphasizing important interior components such as importance-driven transparency, silhouette-based methods, and related approaches to support effective spatial understanding in complex assemblies. Finally, we discuss the unique challenges of rendering transparent constructive solid geometry (CSG) objects, including robustness, correctness, and integration with modern rendering pipelines. Collectively, these contributions characterize current capabilities, systematize open problems, and outline future research directions for transparency rendering in CAD visualization.

cs.GR↗

arXiv2026

mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis

High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and existing datasets provide only 2D range-azimuth maps or sparse point clouds rather than raw analog-to-digital converter (ADC) signals. Hardware scaling is expensive, synthetic-aperture scanning is impractical at fleet scale, and learned synthesis methods are bottlenecked by the very data shortage they aim to address. We present mmIR, an open-source differentiable frequency-modulated continuous-wave (FMCW) radar inverse renderer that fits a physics-based forward model to real captures and re-renders from dense virtual apertures to synthesize high-resolution 3D radar data. Because radar resolution is too coarse to recover geometry directly, mmIR performs LiDAR-assisted inverse rendering: using LiDAR-derived meshes as a geometric scaffold, mmIR optimizes per-vertex International Telecommunication Union (ITU) physics materials, vertex normals, and antenna beam patterns through end-to-end automatic differentiation of a phase-coherent multiple-input multiple-output (MIMO) forward model with multi-bounce propagation, polarization, and free-space diffraction. On seven outdoor and six indoor ColoRadar scenes, mmIR achieves 0.914 mean Pearson correlation on range-azimuth maps versus 0.307 for Sionna-RT. Scenes trained on a cascaded imaging radar transfer to a co-located single-chip radar without re-training (0.554 correlation), and dense virtual arrays (100x100 elements) produce single-frame 3D occupancy validated against LiDAR. Project page: https://mmwave-inverse-rendering.github.io/

cs.CV↗

arXiv2026

Visual Cue Interactions in AR-Guided Needle Insertion: A Prostate Biopsy-Inspired Phantom Study

Despite the apparent simplicity of the motor action involved during percutaneous needle procedures, manipulating the tool's direction becomes challenging when clinicians cannot directly visualize internal anatomy and must rely on ultrasound images, which increase cognitive demand. Augmented reality (AR) offers the promise to assist with these tasks by providing pertinent visual information in the clinician's field of view. However, simply providing visual information that ignores meaningful visual cues can complicate depth perception and spatial understanding. In this work, we introduce and evaluate three visualization techniques for needle alignment developed during design sessions with medical experts: a localized focus-and-context window, a color-based proximity encoding, and an explicit trajectory overlay. These techniques were evaluated in a user study (n=26) including clinical experts (n=7) using a prostate-biopsy-inspired phantom. Results from this study suggest that cue effects depended on the surrounding cue configuration and user expertise. For novices, explicit trajectory overlay improved targeting accuracy and reduced retreat behavior, but its effect on completion time varied across cue configurations, with slower performance when the overlay was presented alone. For experts, the focus-and-context window reduced completion time and retreat events, while color-based proximity overlay improved completion time. Subjectively, color cues were often perceived as helpful even when their effects on accuracy were not consistent. These results suggest that AR guidance strategies for percutaneous interventions should consider user expertise and visual context.

cs.GR↗

arXiv2026

The Role of Mixed and Augmented Reality in Medical Visualization: Literature Review and A Context-Aware Taxonomy

The discovery and evolution of medical imaging technologies have enabled non-invasive visualization of internal anatomy that has be- come essential for supporting diagnosis, monitoring, and treatment. However, because medical imaging relies on complex physical processes and contrast mechanisms for image formation, imaging alone is not sufficient to enable humans to leverage the resulting in- formation fully. In addition, traditional methods to visualize the resulting information use two-dimensional displays to present three-dimensional anatomical structures. The introduction of Augmented and Mixed Reality (AR/MR) technologies offers an opportunity to provide valuable paradigms for medical imaging visualization, allowing users to observe, explore, and interact with anatomical infor- mation in more spatially intuitive ways. However, naive implementation without careful design considerations can lead to perceptual inconsistencies, potentially compromising utility and effectiveness. In this paper, we present a structured taxonomy of medical AR/MR visualization strategies aimed at providing clearer insight into how visualization design varies across clinical use cases. The taxonomy organizes techniques based on four core design components: image modality, data dimensionality, display technology, and clinical application. In addition, we introduce two critical dimensions that are often overlooked in the literature: visualization anchoring (the spatial relationship between virtual content and the physical world), and perceptual awareness (the use of visual cues to support spatial interpretation). Together, these components form a comprehensive taxonomy, offering a detailed framework for selecting appropriate visualization techniques in medical applications.

cs.GR↗

arXiv2026

ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields

We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here for 3D Gaussian Splatting. Our method reformulates training as block coordinate descent over spatial partitions: only one block of parameters is active at a time, while all others are frozen. By exploiting the associativity of alpha blending, these inactive regions can be pre-rendered and collapsed into foreground and background RGBA images. As a result, for fixed partition size and image resolution, peak VRAM becomes O(1) with respect to total scene extent, rather than growing with full scene size. This enables GPUs with limited memory to train scenes that would otherwise not fit in core. In experiments, our method closely preserves the reconstruction quality of 3DGS, with less than 5% PSNR degradation, while ABCD with compositing ablated suffers roughly 40% degradation. Our code can be found at https://github.com/shiukaheng/abcd

cs.CV↗

arXiv2026

Evaluating Constrained Iterative Refinement for Scalable Vector Graphics Generation with Off-the-Shelf VLMs

Scalable Vector Graphics (SVGs) power much of the modern visual ecosystem, yet state-of-the-art generative models focus almost entirely on rasterized images. We explore whether inference-time methods can unlock SVG generation capabilities in off-the-shelf vision-language models (VLMs). We systematically evaluate a constrained iterative refinement harness that combines visual feedback, structured editing, and constrained decoding to characterize the capabilities and limitations of current VLMs for SVG generation. Across multiple VLMs and generation settings, we find that constrained decoding improves compilation success rates, while iterative refinement reveals a deficit in visual reasoning and self-correction. Our results highlight both the promise and current limitations of using inference-time methods to adapt general-purpose VLMs for SVG generation.

cs.CV↗

arXiv2026

Electrospun Fields: 3D Nano-Fiber Material Computation as Design Method

We present a robotic electrospinning platform and design method for depositing nanofiber membranes onto non-planar, three-dimensional conductive geometries. Conventional electrospinning relies on fixed emitters and planar grounded collectors, which restricts deposition to flat substrates: on concave geometries, field shielding prevents fibers from reaching recessed regions, and material bridges across elevated features instead. We address this with a custom end-effector integrated with a six-axis UR20 arm. The tool carries a localized stepper-driven syringe pump that maintains consistent polymer flow independent of orientation, and routes high-voltage DC (up to 25 kV) directly to a robot-mounted needle, turning the arm into a mobile emitter with full kinematic control over position, orientation, working distance, and traversal velocity. Toolpaths that continuously reorient the emitter along surface normals give access to concave topologies unreachable by fixed-axis systems. We characterize the resulting deposition behavior in two parts. A catalog of four bio-compatible polymer systems (PEO, PVA, keratin-PEO, silk-PEO) establishes the operating envelope, reporting deposition speed, jet stability, fiber size, alignment, and durability for each. A taxonomy of 3D-printed conductive scaffolds spanning geometric primitives, hybrid compositions, and square, triangular, and hexagonal lattices links collector geometry to fiber alignment, density, and cross-void bridging. We also demonstrate programmable grounding, in which selectively energized pins in an array steer deposition without changing physical geometry. Assembly instructions and toolpath-generation code are released as an open-source repository.

cond-mat.soft↗

arXiv2026

Lipschitz Extension Initialization for Moving Least Squares Reconstruction from Sparse Irregular Samples

The idea of using Lipschitz extensions [1,2], or Gradually Varied Functions (GVFs)[3], for mesh-free scattered data reconstruction was proposed by the author in 2012 [4]. However, its practical application to modern mesh-free reconstruction methods has not been fully explored. Motivated by recent advances in computational tools, including AI-assisted mathematical programming and software development, we revisit this idea and investigate the use of a Lipschitz extension as an initialization step for Moving Least Squares (MLS) reconstruction [5,6]. Our computational experiments indicate that this initialization significantly improves the stability and reconstruction accuracy of MLS under sparse and irregular sampling. This is a preliminary study intended to establish feasibility; a fuller evaluation with additional benchmarks and comparisons is left to future work.

eess.SP↗

arXiv2026

Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting

Real-time reconstruction of deformable surgical scenes is vital for advancing robotic surgery, improving intraoperative guidance, and enabling automation. Recent methods achieve dense reconstructions from da Vinci robotic surgery videos, with Gaussian Splatting (GS) offering real-time performance via graphics acceleration. However, reconstruction quality in occluded regions remains limited, and depth accuracy has not been fully assessed, as benchmarks like EndoNeRF and StereoMIS lack 3D ground truth. We propose Diff2DGS, a two-stage framework for reliable 3D reconstruction of occluded surgical scenes. First, a diffusion-based video module with temporal priors inpaints tissue occluded by instruments with high spatiotemporal consistency. Second, we adapt 2D Gaussian Splatting (2DGS) with a Learnable Deformation Model (LDM) to capture dynamic tissue deformation and anatomical geometry, and introduce adaptive depth weight to improve geometric fidelity. We further extend evaluation beyond image-quality metrics by performing quantitative depth analysis on the SCARED dataset. Diff2DGS outperforms state-of-the-art methods in both appearance and geometry, reaching 38.02 dB PSNR on EndoNeRF and 34.40 dB on StereoMIS. Our experiments also show that optimizing image quality alone does not necessarily ensure accurate 3D geometry.The code is available at https://diff2dgs.github.io/.

cs.CV↗

arXiv2026

DesigNet: Learning to Draw Vector Graphics as Designers Do

AI-driven content generation has made remarkable progress in recent years. However, neural networks and human designers operate in fundamentally different ways, making collaboration between them challenging. We address this gap for Scalable Vector Graphics (SVG) by equipping neural networks with tools commonly used by designers, such as axis alignment and explicit continuity control at command junctions. We introduce DesigNet, a hierarchical Transformer-VAE that operates directly on SVG sequences with a continuous command parameterization. Our main contributions are two differentiable modules: a continuity self-refinement module that predicts $C^0$, $G^1$, and $C^1$ continuity for each curve point and enforces it by modifying Bézier control points, and an alignment self-refinement module with snapping capabilities for horizontal or vertical lines. DesigNet produces editable outlines and achieves competitive results against state-of-the-art methods, with notably higher accuracy in continuity and alignment. These properties ensure the outputs are easier to refine and integrate into professional design workflows. Source Code: https://github.com/TomasGuija/DesigNet.

cs.CV↗

arXiv2025

Wavelet-Space Representations for Neural Super-Resolution in Rendering Pipelines

We investigate the use of wavelet-space feature decomposition in neural super-resolution for rendering pipelines. Building on recent neural upscaling frameworks, we introduce a formulation that predicts stationary wavelet coefficients rather than directly regressing RGB values. This frequency-aware decomposition separates low- and high-frequency components, enabling sharper texture recovery and reducing blur in challenging regions. Unlike conventional wavelet transforms, our use of the stationary wavelet transform (SWT) preserves spatial alignment across subbands, allowing the network to integrate G-buffer attributes and temporally warped history frames in a shift-invariant manner. The predicted coefficients are recombined through inverse wavelet synthesis, producing resolution-consistent reconstructions across arbitrary scale factors. We conduct extensive evaluations and ablations, showing that incorporating SWT improves both fidelity and perceptual quality with only modest overhead, while remaining compatible with standard rendering architectures. Taken together, our results suggest that wavelet-domain neural super-resolution provides a principled and efficient path toward higher-quality real-time rendering, with broader implications for neural rendering and graphics applications.

cs.GR↗

arXiv2025

SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting

Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision or costly annotations), robustness issues (being susceptible to local optima), and rendering shortcomings (lacking speed or photorealism). We introduce SplArt, a self-supervised, category-agnostic framework that leverages 3D Gaussian Splatting (3DGS) to reconstruct articulated objects and infer kinematics from two sets of posed RGB images captured at different articulation states, enabling real-time photorealistic rendering for novel viewpoints and articulations. SplArt augments 3DGS with a differentiable mobility parameter per Gaussian, achieving refined part segmentation. A multi-stage optimization strategy is employed to progressively handle reconstruction, part segmentation, and articulation estimation, significantly enhancing robustness and accuracy. SplArt exploits geometric self-supervision, effectively addressing challenging scenarios without requiring 3D annotations or category-specific priors. Evaluations on established and newly proposed benchmarks, along with applications to real-world scenarios using a handheld RGB camera, demonstrate SplArt's state-of-the-art performance and real-world practicality. Code is publicly available at https://github.com/ripl/splart.

cs.CV↗

arXiv2024

A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions

We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. Typical Poisson discretizations yield large, ill-conditioned linear systems. Iterative solvers can be effective for these problems, but only when equipped with powerful preconditioners. Unfortunately, effective preconditioners like multigrid require costly setup phases that must be re-executed every time domain shapes or boundary conditions change, forming a severe bottleneck for problems with evolving boundaries. In contrast, we present a neural preconditioner trained to efficiently approximate the inverse of the discrete Laplacian in the presence of such changes. Our approach generalizes to domain shapes, boundary conditions, and grid sizes outside the training set. The key to our preconditioner's success is a novel, lightweight neural network architecture featuring spatially varying convolution kernels and supporting fast inference. We demonstrate that our solver outperforms state-of-the-art methods like algebraic multigrid as well as recently proposed neural preconditioners on challenging test cases arising from incompressible fluid simulations.

math.NA↗

arXiv2023

Deep and Fast Approximate Order Independent Transparency

We present a machine learning approach for efficiently computing order independent transparency (OIT). Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setup and is portable to all platforms running even with commodity GPUs. Our method requires a rendering pass to extract all features that are subsequently used to predict the overall OIT pixel color with a pre-trained neural network. We provide a comparative experimental evaluation and shader source code of all methods for reproduction of the experiments.

cs.GR↗

Compare source metadata on this page

Work	Published	Source identifier	Source
Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges	2026-08-28	2608.28310	arxiv
mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis	2026-08-28	2608.28913	arxiv
Visual Cue Interactions in AR-Guided Needle Insertion: A Prostate Biopsy-Inspired Phantom Study	2026-08-27	2608.27620	arxiv
The Role of Mixed and Augmented Reality in Medical Visualization: Literature Review and A Context-Aware Taxonomy	2026-08-27	2608.27644	arxiv
ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields	2026-08-27	2608.27735	arxiv
Evaluating Constrained Iterative Refinement for Scalable Vector Graphics Generation with Off-the-Shelf VLMs	2026-08-26	2608.28678	arxiv
Electrospun Fields: 3D Nano-Fiber Material Computation as Design Method	2026-08-24	2609.05487	arxiv
Lipschitz Extension Initialization for Moving Least Squares Reconstruction from Sparse Irregular Samples	2026-08-18	2609.02918	arxiv
Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting	2026-07-28	2602.18314	arxiv
DesigNet: Learning to Draw Vector Graphics as Designers Do	2026-04-07	2604.06494	arxiv
Wavelet-Space Representations for Neural Super-Resolution in Rendering Pipelines	2025-09-20	2508.16024	arxiv
SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting	2025-06-04	2506.03594	arxiv
A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions	2024-06-14	2310.00177	arxiv
Deep and Fast Approximate Order Independent Transparency	2023-05-17	2305.10197	arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.