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Gaussian Process-Augmented Model Predictive Contouring Control for 6-DoF Trajectory Tracking with a Holonomic AUV
2026-11-10 17:30:20David D\"orner, Giancarlo Troni, Ignacio Torroba, Ivan SteniusAbstract
Small, holonomic Autonomous Underwater Vehicles (AUVs) enable close-range inspection of underwater structures from arbitrary viewpoints, requiring accurate position and attitude tracking under state and input constraints. Model Predictive Contouring Control (MPCC) naturally handles these requirements, but relies on an accurate dynamics model, which is difficult to obtain underwater. In this paper, we present a nonlinear MPCC in all six Degrees of Freedom (DoF) for a holonomic AUV and alleviate the inaccuracies in the dynamics by augmenting the nominal model employed with residual dynamics learned via Gaussian Process (GP) regression. The learned residual is embedded in the optimal control problem through a zero-order robust optimization scheme, which keeps the controller real-time capable at 25 Hz. A simulation ablation shows that residual selection is essential when designing the controller, and simulation and in-water experiments with the Multimodality, Observing, Low-cost, Agile (MOLA) AUV validate the resulting controllers against a nominal MPCC and a field proven Proportional-Integral-Derivative (PID) controller.
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https://arxiv.org/abs/2611.14373
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Region-aware error triage for medical image segmentation with groupwise conformal risk control
2026-11-06 04:15:28Nishi Kanta Paul, Md Shihabul Islam Shovo, Hoileong LeeAbstract
Segmentation masks are increasingly reviewed rather than redrawn, which requires knowing where a mask is likely wrong. Learned error maps rank pixels by risk, but the number of pixels to warn is chosen ad hoc, and calibrating one warning threshold over all error pixels may leave errors far from the predicted contour under-flagged. We propose RACE-Triage, a post-hoc error triage framework that leaves the segmentation mask unchanged. A risk network is trained on out-of-fold predictions from backbone-independent inputs. The predicted contour partitions each image into a boundary band and a non-boundary region, and conformal risk control is applied separately in each region to bound the expected fraction of missed errors among cases with errors there. On ISIC 2018 dermoscopy and BUSI breast ultrasound (where patient identifiers are unavailable, so independence across images is assumed), pooled control met its overall target but missed on average 31.8% and 36.4% of non-boundary errors. Regional control reduced these fractions to 19.3% and 20.9% and raised the recall of size-filtered false-negative components from 0.722 to 0.818 and from 0.611 to 0.763. The mean warned area grew by 1.6 and 4.9 percentage points, and its 95th percentile reached 23.5% and 38.5%. The effect held across three seeds and a second backbone, and area-matched heuristics did not reproduce it. On the external PH$^2$ set, frozen thresholds exceeded both targets, whereas recalibration with 100 local images brought the mean over repeated splits below them. Regional calibration improves non-boundary error coverage at a measured increase in warned area.
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URL
https://arxiv.org/abs/2611.08962
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FoleyForge: Free-form, Controllable, Rectified-flow Editing of Sound Effects
2026-11-05 20:52:06Giovana Morais, Hugo Flores Garc\'ia, Prem Seetharaman, Johannes Imort, Justin Salamon, Magdalena Fuentes, Oriol NietoAbstract
We present FoleyForge, a generative model for instruction-based editing of sound effects. Given an input clip and a free-form instruction describing the desired change, it applies the edit while preserving the input's identity. A single model handles semantic, stylistic, and DSP-like changes. We introduce a data pipeline that guides an audio language model to author both DSP-based acoustic edits and similarity-preserving semantic edits, thus addressing the lack of publicly available audio editing data. Such data is then used to fine-tune a rectified-flow audio generation model, which we extend to expose attribute controls for loudness change, edit strength, and an input-derived contour envelope, each tunable via classifier-free guidance, yielding a more expressive editing model. FoleyForge achieves state-of-the-art instruction adherence at competitive audio quality and source faithfulness.
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https://arxiv.org/abs/2611.08695
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InstantHand: Feed-Forward 3D Gaussian Splatting for Animatable Hand Avatar Reconstruction from a Single Image
2026-11-05 17:21:08Chanho Kim, Muhammad Salman Ali, Kyeonghwan Gwak, Muhammad Shaheryar, Incheol Park, Jeongwan On, Seungryul BaekAbstract
Hand avatar reconstruction typically relies on multi-view capture or per-subject optimization, limiting its scalability. We introduce InstantHand, a feed-forward framework that constructs an animatable 3D Gaussian hand avatar from a single image using off-the-shelf MANO, camera, and mask estimates, without test-time optimization. Image-averaged losses provide insufficient supervision for alpha leakage, which thickens thin finger contours. To address this issue, we propose a Soft Alpha Band regularizer that concentrates supervision on silhouette boundaries. Furthermore, colors regressed from image features can drift toward a learned prior even in observed regions. Our Reliability-gated Appearance Transfer preserves observed appearance by transferring source colors according to each Gaussian's rendering contribution, while completing unobserved regions using the learned prior conditioned on global source identity. InstantHand constructs an avatar in 2.84 seconds and renders at 161 FPS, matching per-subject-optimized baselines on InterHand2.6M and remaining competitive in zero-shot evaluation on HanCo.
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https://arxiv.org/abs/2611.08330
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CardioDrift: Automatic Cardiac Motion Tracking on a 3D Atlas Mesh via Shape-aware Coherent Point Drift with Slice Shift Correction
2026-11-04 15:39:07Reza Akbari Movahed, Abuzar Rezaee, Shatha Alamri, Edmond S. L. Ho, Fani Deligianni, Ali GooyaAbstract
Automatic cardiac motion tracking from cine cardiac magnetic resonance (CMR) imaging is essential for quantifying myocardial motion abnormalities associated with cardiovascular disease. Existing methods fall into two groups: those that represent motion without anatomical correspondence across subjects and therefore limit inter-subject motion assessment; and those that represent motion with globally corresponding points, which typically require a time-consuming and error-prone semi-automatic step to fit a cardiac atlas to the subject space. To address these limitations, this study proposes CardioDrift, a fully automatic, atlas-based probabilistic framework that represents subject-specific cardiac motion on a 3D atlas mesh with globally corresponding points. The Coherent Point Drift (CPD) formulation is generalised to jointly register the atlas surfaces of multiple cardiac regions to the anatomically corresponding contours extracted from cine CMR segmentation maps throughout the cardiac cycle. Slice-shift correction is embedded in the same probabilistic model, estimating slice-wise translations that suppress the misalignment artefact limiting atlas-fitting accuracy. Evaluation on 1301 randomly selected UK Biobank subjects and the M&M dataset, CardioDrift attains Dice similarity coefficients (DSCs) of $92.23\pm8.70\%$, $82.11\pm7.56\%$, and $88.69\pm4.13\%$ for the left ventricle (LV), myocardium (Myo), and right ventricle (RV), exceeding the strongest baseline on every distance and overlap metric of both datasets.
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https://arxiv.org/abs/2611.06664
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BAM: Bivariate Additive Models for Interpretable Tabular Prediction
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Generalized Additive Models with pairwise interactions (GA$^2$Ms) are regarded as glass-box models, since each pairwise shape function can be visualized as a contour plot. In practice, the shape functions learned by methods such as EBM or NODE-GAM are so complex (irregular contours and sharp local fluctuations) that a practitioner cannot meaningfully interpret them or check them against domain knowledge. Auditability in this stronger sense requires substantially simpler shape functions. We argue that such simplification can match the accuracy of standard GAMs if the right feature pairs are captured. We propose Bivariate Additive Models (BAMs), a class of GA$^2$Ms in which every pairwise shape function is constrained to a simple parametric form, instantiated as an asymmetric bivariate Gaussian. Each component is visualizable as a unimodal contour map, and its feature pair is selected automatically during training. Experiments on six tabular benchmarks show that BAM attains predictive accuracy comparable to standard GAMs and approaches that of GA$^2$Ms. We also show the trade-off between accuracy and the number of components to inspect.
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URL
https://arxiv.org/abs/2611.05940
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High-fidelity training-free photovoltaic module mapping from multi-view UAV imagery
2026-11-04 04:03:53WenHao Duan, Qi Chen, Penglei Zhang, Ruizhe Deng, Zhiling Guo, Haoran Zhang, Yuntian ChenAbstract
High-fidelity photovoltaic (PV) module mapping requires complete instance recovery and accurate delineation. Vision foundation models offer zero-shot segmentation, but converting incomplete and inconsistent predictions across views into a complete, geometrically accurate map of individual modules remains challenging. We propose the Multi-View PV Module Mapper (MV-PVMapper), a training-free framework coupling vision foundation models with unmanned aerial vehicle (UAV) photogrammetry. First, Segment Anything Model 3 (SAM 3) generates candidates from original images using text prompts. Their contours are then transferred into object space through per-module slope correction to limit distortion from surface-model noise, and verified against representative module dimensions to guide cross-view instance selection. Finally, accepted instances return to all covering images as box prompts for SAM~3, supplementing text prompts with spatial cues for iterative segmentation, verification and fusion. On five-direction surveys of three operational rooftop PV plants at approximately 1 cm nadir ground sampling distance, two feedback iterations recover 7,710 of 7,711 reference modules with no false positives at an intersection-over-union (IoU) matching threshold of 0.5. Macro-averaged area precision and recall reach 99.00 % and 98.52 %, respectively. Averaged across sites, the aggregated Jaccard index reaches 97.55 %, compared with 60.73 % and 41.24 % for orthomosaic segmentation and multi-view pixel-voting baselines, respectively; average precision at the stricter IoU threshold of 0.95 reaches 95.67 %. Ablation studies show that module-size constraints are critical for instance separation, while slope correction and feedback to all covering views improve boundary accuracy. These results are achieved without task-specific image annotation or segmentation-model weight updates.
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URL
https://arxiv.org/abs/2611.05131
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Risk Control Certifies Rates, Not Magnitudes: An Audit of Selective Fetal Biometry
2026-11-04 02:41:56Zhengshuai Yang, Linna Jiang, Changyun Li, Guihu ZhaoAbstract
Prenatal ultrasound biometry must return a head circumference (HC) and refuse when the image cannot support one. Selective prediction trades coverage against error, but its finite-sample guarantees bound the rate of gross errors, not their magnitude. We audit a tool-grounded biometry pipeline on two separately pre-registered four-way splits of the 999 labelled HC18 images, retraining the segmenter three times on each. Calibrating the contour-completeness threshold by exact Clopper-Pearson risk control returns a certificate indistinguishable from a hand-set floor, yet the pipeline still emits a 271.3 mm error: a small, geometrically complete ring fitted to an image whose true HC is 324.6 mm. The accepted candidate scores 0.94 completeness, so the score cannot see the failure, and one outlier among $\approx 150$ answers leaves the certified risk bound ($\alpha = 0.10$) intact. A second, untouched split reproduces the mechanism on a different image (233.3 mm). Constraining the admissibility region---the anatomical range in which the measurand is defined---removes the family instead: raising the lower bound from 50 to 55 mm costs 0.4 coverage points and cuts the worst-case error on both splits to 11.4 and 11.1 mm, whereas suppressing it through the completeness threshold costs 19.0 and 12.1 points. At an anatomically defined 70 mm the frozen tests give 1.91 and 1.81 mm MAE.
Abstract (translated)
URL
https://arxiv.org/abs/2611.05851
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Anchored Slice Propagation with Boundary Transport for 3D CT Segmentation from Three Orthogonal Annotated Slices
2026-11-03 22:21:39Tushar Kataria, Md Rakibul Haque, Shireen Y. ElhabianAbstract
Sli2Vol propagates a binary mask through affinities between adjacent slices, but an error can persist because each prediction becomes the input to the next step. Three orthogonal annotations reduce dependence on any one chain, yet they offer more than three starting masks: where a chain crosses another annotated plane, the correct label is already known. Those labels could correct drift during propagation and indicate how much to trust each chain at fusion. Between annotated planes, the information carried by the chain may matter as well. A binary mask records foreground membership but discards distance to the boundary, a cue that could help follow gradual shape changes between this http URL address these two sources of error with a fixed pretrained affinity backbone. We anchor each chain by correcting its prediction where it crosses another annotated plane, then use its agreement with those labels to weight fusion. We compare this anchored mask with two ways of transporting boundary geometry: a truncated signed-distance field (SDF) propagated by dense affinity averaging, and a contour whose vertices follow motion cues from the same affinity. The contour tracker starts from a hand-crafted rule and learns a motion correction from supervoxel pseudo-objects in 506 CT volumes, without manual organ labels. On 2,091 held-out structure instances from approximately 403 CT scans spanning 17 abdominal structures, anchoring raises mean Dice from 0.737 with orthogonal mask propagation to 0.769 and improves every structure. Beyond the anchored mask, learned contour tracking improves Dice by 5.7\% for the esophagus and 5.9\% for the postcava, while SDF transport improves right-adrenal Dice by 2.8\%. The anchored methods have similar mean Dice (0.769--0.772); contour tracking performs worse on the kidneys, stomach, and hepatic vessel tree.
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URL
https://arxiv.org/abs/2611.05693
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ARIDA - Active railway infrastructure dataset annotation using neural network predictions
2026-11-03 21:31:10Maximilian Dornik, Christian Sieberichs, Simon Geerkens, Alexander BraunAbstract
Modern railway perception requires large comprehensive image datasets, but creating them is slowed by manual labeling, especially for track polylines and other complex infrastructure geometry. This paper presents the Active Railway Infrastructure Dataset Annotation framework, a railway-specific semi-automated pipeline that converts instance predictions from a mask scoring instance segmentation network into bounding-boxes, polygons, polylines, and paired polylines. To keep automatic track geometry annotation reliable without ground truth, it applies confidence thresholds and an unique contour-agreement quality score as an acceptance gate. An active learning loop ranks remaining images by uncertainty and adapts the number of manual queries per cycle. This is achieved through a novel quantity control mechanism, in which easy images are auto-accepted and human effort targets hard cases. Experiments on disjoint, diverse imagery and time-sequential, redundant imagery quantify effort reduction and accuracy trade-offs, achieving about two thirds reduction in the disjoint regime and up to 93% in the sequential regime. The study also provides guidance for initial label budgets and stopping to avoid over-annotation.
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URL
https://arxiv.org/abs/2611.05646
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SketchDirector: Complex Scene Sketching via Agentic Layout Reasoning and Detail Refinement
2026-10-30 05:05:12Yiqi Liang, Xiaodong CunAbstract
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects or simplified scenes, leaving text-guided complex scene sketching underexplored. We present SketchDirector, an agentic framework that mimics the human sketching workflow for automatic multi-concept scene sketching. Given a scene-level text prompt, SketchDirector first employs a large language model~(LLM) to decompose the scene into multiple draft assets, where each is represented by a set of Bézier curves. Then, an agentic reasoning loop iteratively plans and executes their spatial arrangement, guided by self-evolving memory. Finally, a detail refinement module leverages diffusion priors to enhance sketch quality while preserving geometric structure. To support future research and applications in multi-concept scene sketch generation, we further introduce the \textit{MCSketch8K} dataset, comprising editable vector assets, compositional layouts, and object-level manipulation annotations. Extensive experiments show that SketchDirector outperforms state-of-the-art baselines in semantic and compositional quality while effectively preserving overall contour organization during refinement. The project page is: \href{this https URL}{this https URL}.
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URL
https://arxiv.org/abs/2610.41260
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Segment Anything Pancreas: A Unified MRI Segmentation Model for Pancreatic Cancer Radiotherapy
2026-10-29 23:18:16Zach Eidex, Mojtaba Safari, Kundan Thind, Parag J. Parikh, Zhen Tian, Xiaofeng YangAbstract
Background: Limited tumor annotations and incomplete labels across MRI datasets hinder unified segmentation of pancreatic tumors and organs at risk (OARs) for MRI-guided radiotherapy. Purpose: We propose Pancreas-MedNeXt, a unified model that segments the whole pancreas, its subregions, tumors, and surrounding OARs from one MRI volume. Methods: The model uses a shared MedNeXt encoder-decoder with supervised whole-pancreas and subregion pretraining, followed by multitask fine-tuning. Three output heads predict the pancreatic subregions, tumor, and OARs (stomach, duodenum, kidneys). An annotation-aware loss allows training on partially labeled data without treating unannotated structures as background. Pancreas and tumor were evaluated by three-fold cross-validation on 142 PANTHER patients (92 diagnostic, 50 MR-Linac), subregions and OARs on Cyst-X and AMOS test sets, and pancreas and OARs on 33 external T1-weighted TotalSegmentator scans. Comparisons with nnU-Net, MedNeXt, VISTA3D, and Swin UNETR used the Dice similarity coefficient (DSC) with paired bootstrap 95% CIs. Results: Pancreas-MedNeXt achieved the highest mean pancreas and tumor DSC on diagnostic and MR-Linac MRI. Tumor DSC was 58.41% and 42.55%, exceeding task-specific MedNeXt by 4.94 (95% CI: 0.28-9.45) and 5.89 (95% CI: 2.37-9.72) percentage points, respectively. Whole-pancreas DSC was 85.73% and 70.66%. Pretraining improved head, body, and tail DSC by 2.01, 3.00, and 3.78 percentage points over training from scratch. It also achieved the highest DSC for the pancreas, duodenum, and right kidney on AMOS, and for all five organs on external MRI, exceeding the next-best method by 4.92-8.85 percentage this http URL: A single Pancreas-MedNeXt model segments tumors, pancreatic subregions, and OARs on diagnostic and MR-Linac MRI, which could streamline contouring for pancreatic cancer radiotherapy.
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https://arxiv.org/abs/2610.41063
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Inter-Frame Predictive Contour Coding for Semantic Map Compression
2026-10-29 19:07:28Runyu Yang, Hyomin Choi, Fabien Racap\'e, Ivan V. Baji\'cAbstract
Dynamic semantic maps have become an important intermediate representation for many computer vision applications. Efficient lossless compression of such maps is essential for reducing storage and transmission costs. Existing contour-based semantic map compression methods mainly exploit spatial redundancy within individual frames while largely ignoring the strong temporal correlation between adjacent semantic maps. In this paper, we propose an inter-frame predictive contour coding framework for lossless semantic map compression. The proposed framework first predicts blob positions and label indexes from the reference frame and then performs contour prediction using corresponding chain-code units in the reference contours. A mode selection mechanism is further employed to choose between intra and inter coding according to the estimated coding cost. By exploiting temporal contour redundancy while preserving the advantages of contour-based representation, the proposed framework significantly improves coding efficiency. Experimental results on the VSPW dynamic semantic segmentation dataset demonstrate that the proposed method consistently outperforms state-of-the-art lossless semantic map compression methods. Compared with the recent context-adaptive extended chain coding framework, the proposed inter-frame coding achieves an average bitrate reduction of 3.0\% without significant increase in the average encoding time, demonstrating an effective trade-off between compression performance and computational complexity. The source code is made publicly available at this https URL.
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URL
https://arxiv.org/abs/2610.40887
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TRUCO: A Scalable Lock-Free Algorithm for Parallel Contour Extraction
2026-10-27 14:18:47Rafael Munoz-Salinas, Francisco J. Romero-Ramirez, Manuel J. Marin-JimenezAbstract
Transforming binary masks into geometric contours remains a critical step in many computer vision pipelines, particularly for post-processing high-resolution segmentation outputs. Yet, standard algorithms such as the Suzuki-Abe method are limited by their sequential nature and the overhead of building topological hierarchies. As modern applications transition to high-resolution imaging, there is a critical need for contour extraction that leverages multi-core CPU architectures without the complexity of tile stitching or heavy synchronization. This paper presents TRUCO (Threaded Raster Unrestricted Contour Ownership), a lock-free parallel algorithm designed for high-performance geometric boundary extraction. TRUCO introduces a start-point ownership rule, combined with a speculative tracing model, in which threads trace contours downward into adjacent regions and use an upward-termination logic to ensure each contour is processed exactly once. Experimental results on synthetic and real-world datasets, conducted on both consumer and server-grade CPUs, demonstrate that TRUCO significantly outperforms existing baselines. The method achieves speedups of over an order of magnitude compared to the industry standard while maintaining robust scalability on high-resolution scenes across diverse hardware architectures.
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URL
https://arxiv.org/abs/2610.37297
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EEG-Based Decoding of Musical Mode and Melodic Direction Using Empirical Mode Decomposition
2026-10-27 12:47:51Sergio Guill\'en, Lorenzo J. Tard\'on, Ana M. Barbancho, Isabel BarbanchoAbstract
Electroencephalography (EEG) has proven to be a powerful tool for decoding neural activity in real-life scenarios, including the perception and processing of emotions elicited by music. Among the various musical features that modulate these responses, musical mode and melodic direction (or contour) play a central role. This work proposes a novel EEG feature extraction framework based on Empirical Mode Decomposition (EMD) to decompose EEG signals recorded during active piano performance for the detection of these two musical features. Energy-based descriptors were computed from the first four Intrinsic Mode Functions (IMFs) obtained through EMD and used to train a Wide Neural Network classifier. The model performance was benchmarked against several standard classifiers commonly employed in EEG classification tasks. Two experimental scenarios were explored: intra-subject and intersubject. In the former case, the proposed framework achieved average accuracies of 84%, whereas in the inter-subject case, accuracies reached 81% on average using the first IMF, with AUC-ROC values of 0.9 on average in both cases. A frequency-domain analysis revealed a strong correspondence between the IMFs and conventional EEG sub-bands, except for certain specific frequency components that were not fully represented. Predominant activations were observed over the frontal, temporal, and parietooccipital regions, areas consistently associated with music processing. Furthermore, the proposed method demonstrated robustness to handedness, successfully classifying EEG data across participants regardless of their dominant hand. These findings highlight the potential of EMD-based feature extraction for identifying music-related neural patterns and contribute to advancing research in Music Information Retrieval (MIR), affective neuroscience, and the broader understanding of the neural encoding of musical structure.
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URL
https://arxiv.org/abs/2610.37148
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O2D-MedNet: An Optimization-Aware Dual-Domain Framework for Robust Medical Image Segmentation
2026-10-26 11:11:47Shipeng Hu, Miaohui Wang, Jing Qin, Sukun Tian, Yuchun SunAbstract
Medical image segmentation (MIS) remains challenging due to composite degradations and multi-scale geometric variations, which jointly impair lesion-background separability, structural consistency, and optimization stability. Existing single-stream frameworks entangle heterogeneous cues within a single representation pathway, while hybrid-stream approaches often rely on generic or static fusion, limiting effective coordination across domains and failing to explicitly address optimization instability. To overcome these limitations, we propose a new optimization-aware dual-domain framework for MIS (O2D-MedNet). First, a degradation-aware dualdomain interaction (DDI) module integrates Fourier - and Wavelet-domain features to suppress degradation-sensitive responses while preserving structural and fine-detail information. Second, a cross-backbone contour-guided fusion (CGF) module enables selective interaction between CNN-based local detail modeling and visual state-space-based global structure modeling, improving boundary accuracy and representation consistency. Third, a gradient-context optimization (GCO) strategy stabilizes training by adaptively adjusting learning dynamics based on loss trends and gradient statistics, mitigating instability caused by class imbalance and heterogeneous sample difficulty. Extensive experiments on six benchmarks demonstrate consistent improvements over state-of-the-art MIS methods across multiple imaging modalities. Further analyses confirm enhanced sensitivity, structural consistency, and optimization stability, validating the effectiveness of the proposed O2D-MedNet for robust MIS under heterogeneous conditions.
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URL
https://arxiv.org/abs/2610.35220
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Explicit Frictional Contact Modeling in Neural Garment Simulation
2026-10-24 06:41:28Thanh-Nguyen Truong, Vu-Hoang Tran, Ching-Chun HuangAbstract
Graph neural networks (GNNs) simulate garments efficiently through physics-based self-supervision, but leave body-garment friction implicit or inside training energies, with no friction coefficient $\mu$ to set at inference; retraining the tested GNNs with a coefficient-specific friction term barely responds to $\mu$. Optimization-based solvers decide each contact's stick-slip state from the reaction that governs it, but only by re-optimizing the garment state at every step; a friction term in a one-pass training objective acts as a fixed-weight penalty on sliding, and the trained weights apply no per-contact Coulomb condition at inference. We therefore resolve friction explicitly after prediction: a Modular Contact Operator, attached to a frozen predictor without retraining or an iterative solve, estimates the normal load, applies a closed-form Coulomb correction along a path across body triangles, and propagates the induced motion through the cloth as a linearized membrane wave. On HOOD and ContourCraft, it raises sticking and lowers slip monotonically in $\mu$, tracking a reference solver's response while coefficient-specific retraining stays flat, with low penetration and reference-consistent stretching at an eighth of the solver's runtime.
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URL
https://arxiv.org/abs/2610.33492
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Can One Hear Geometry? Gauge-Invariant Spectral Descriptors for Text-to-B-rep Retrieval
2026-10-24 06:20:11Hongzhu Yi, Hao Liu, Tao Yu, Tiankun Yang, Yujia Yang, Zhenghao Zhang, Tianyu Zong, Zhenyu Guan, Bingkang Shi, Haopeng Jin, Yuanxiang Wang, Zhongtian Luo, Jungang XuAbstract
Text-to-3D solid retrieval requires aligning natural language with precise geometry despite variations in pose, scale, and recording conventions. Existing methods discretize Boundary Representation (B-rep) models into point clouds or meshes, or learn features from faces, edges, and topology, risking information loss or lacking explicit invariance guarantees. We propose a text-to-B-rep retrieval framework based on group actions and spectral geometric invariants, making invariance an explicit constraint on descriptor construction. Specifically, we use group actions to characterize equivalence transformations induced by translation, rotation, reflection, uniform scaling, and contour recording conventions. For sketch-extrusion solids, we construct a fixed 280-dimensional spectral geometric descriptor by integrating geometric information from three complementary levels, including cross-sectional regions, contour boundaries, and extrusion structures. Furthermore, we use descriptor-consistent multi-positive sets and geometric attribute supervision to promote structural alignment between text and solids. On Text-to-3D solid retrieval, our method achieves 88.6% Recall@5, exceeding the previous state of the art by 13.6 percentage points and demonstrating the effectiveness of explicit geometric invariants for precise 3D solid retrieval.
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URL
https://arxiv.org/abs/2610.33484
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Gate3DGen: A Pre-processing Model that Selectively Purifies Real-World Images for 3D Generation
2026-10-22 17:57:02Xiaolin Li, Zihao Huang, Fanpeng Meng, Chen Yang, Guanjun Wu, Kai Zhang, Jiemin Fang, Qi TianAbstract
Single-image 3D generation has achieved high fidelity on curated benchmarks, yet degrades sharply on real-world images with uneven illumination, blur, and occlusions. Purifying such inputs requires deciding when to intervene and preserving the visual cues needed for 3D generation, while leaving clean inputs unchanged. We introduce Gate3DGen, a selective pre-processing model that gates image inputs to existing 3D generators without retraining them. A degradation-aware router bypasses inputs classified as clean and extracts features to guide purification of the remaining inputs. Conditioned on these features, a 3D-oriented one-step purifier purifies degraded inputs with spatial losses that encourage preservation of object identity, contours, and textural cues. To provide paired supervision, we construct a synthetic data engine that renders clean views of 3D assets and synthesises composite degradations. On the synthetic benchmark, the purifier improves image fidelity and downstream 3D geometry in only 2.3s on average, reducing TRELLIS.2 mesh distance by 61.5% relative to degraded inputs. VLM evaluation and human preferences on real photographs further support identity preservation and degradation correction. More demos and code are available at this https URL.
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https://arxiv.org/abs/2610.31649
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Long-wave Infrared Polarimetric Remote Sensing Image Fusion for Vehicle Detection
2026-10-19 13:22:25Wenjuan Zhang, Xiangrui Peng, Zhen Li, Caimeng Yi, Xuesong Ma, Jianwei Wang, Jie PanAbstract
Blurred vehicle contours and low contrast limit detection performance in infrared remote sensing imagery, while existing fusion methods may introduce polarization noise and fail to integrate complementary information effectively. To address these limitations, we propose a Mamba-based infrared polarimetric remote sensing image fusion model. Infrared polarization information reflects surface material properties and geometry, providing complementary information to infrared intensity. The proposed method first combines long-wave infrared total intensity S0 and the degree of linear polarization (DoLP), using a physics-inspired polarization-response proxy and spatial structural continuity to extract guidance features. It then employs bidirectional state space scanning along horizontal, vertical, and both diagonal orientations to enable cross-modal feature fusion across the entire image. Prior-guided multi-window structural similarity, reconstruction constraints, and a relative lower-bound loss for polarization saliency are jointly introduced to suppress noise while preserving global background structures and target polarization features. In addition, we construct AIR-LWIR-Pol, a dataset of 1,901 accurately registered image pairs acquired using an airborne division-of-aperture long-wave infrared polarimetric imaging system, and evaluate downstream vehicle detection using YOLO11. The experimental results show that the proposed method outperforms the comparison methods on four fusion metrics, including entropy and structural similarity. Vehicle detection mAP50 and mAP50-95 reach 55.0% and 29.4%. These results demonstrate the effectiveness of combining physical priors with state space modeling for long-wave infrared polarization image fusion and support its use for target detection in complex remote sensing scenes.
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https://arxiv.org/abs/2610.27018
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