paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation

Haiyang Yan, Jinyue Guo, Yanchao Zhang, Bingqing Wang, Zhenchen Li, Jing Liu, jiazheng liu, Linlin Li, Hua Han

Accurate 3D neuron segmentation in fluorescence microscopyis critical for neuroscience. However, the sparse and elongated morphol-ogy of neurons poses significant challenges to existing segmentation meth-ods. These methods struggle to preserve both local details and globaltopology, leading to fragmented results. To address this, we proposeNeuroRefiner, a multi-agent system that formalizes the human expertworkflow involving iterative global observation and local editing. Specifi-cally, NeuroRefiner comprises three collaborative agents dedicated to di-agnosing topological errors, generating correction instructions, and vali-dating refinement quality. To facilitate agent instruction-guided segmen-tation refinement, we propose TopoRefineNet, a dedicated 3D U-Net-based tool that leverages cross-modality feature fusion to generate refinedmasks. Through multi-round agent reasoning and voxel-level editing,NeuroRefiner produces topologically more accurate segmentations withenhanced interpretability. Experiments on the BigNeuron, CWMBS, andZBFWB datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably achieving a 3.02% improvement in F1 score onthe challenging ZBFWB dataset.

ECCV 2026

LUCE: Constrained Curve-Domain Guidance for Training-Free Low-Light Enhancement with Hue-Preserving Decoupling

Yijie Wei, Weiran Li, Yeqiang Liu, Mina Han, Xue Liu, Zhenbo Li

Low-light image enhancement (LLIE) on pretrained diffu-sion models is commonly achieved through weighted pixel-space guidancelosses, whose competing gradients often destabilize sampling and inducecolor distortion. We present LUCE, a training-free framework that re-formulates sampler-side guidance as a constrained curve-domain lumi-nance control problem. Instead of directly optimizing pixel intensities,LUCE projects luminance guidance into a low-dimensional monotoniccurve space and enforces a unified luminance target via a single scalarenergy defined on a piecewise-linear LUT. This constrained parameteri-zation restricts guidance to a globally consistent exposure trajectory andremoves the need for balancing multiple heterogeneous losses. To preservechromatic fidelity, we introduce a hue-preserving chromatic decouplingmechanism that realizes luminance edits through per-pixel scalar gainswhile maintaining RGB direction. Implemented as a gradient-based cor-rection within a frozen DDPM sampler, LUCE requires no retraining orarchitectural modification. Experiments on paired and unpaired bench-marks demonstrate stable exposure adjustment and improved color fi-delity over prior training-free diffusion baselines.

ECCV 2026

Let ViT Speak: Generative Language-Image Pre-training

Yan Fang, Mengcheng Lan, Zilong Huang, Weixian Lei, Yunqing Zhao, Yujie Zhong, Yingchen Yu, Qi She, Yao Zhao, Yunchao Wei

In this paper, we present Generative Language-Image Pre-training (GenLIP), a simplified generative pretraining approach for Vi-sion Transformers (ViTs) tailored to multimodal large language models(MLLMs). To better align ViTs with the autoregressive nature of LLMs,GenLIP trains a ViT to predict language tokens directly from visual to-kens using a standard language modeling objective—without contrastivebatch construction or an additional text decoder. GenLIP offers three keyadvantages: (1) Simplicity: a single transformer jointly models visualand linguistic tokens; (2) Scalability: it scales efficiently with both dataand model size; (3) Performance: it achieves competitive or superiorresults across diverse multimodal benchmarks. With only 1/5 of theseen samples, GenLIP matches or surpasses strong baselines such asSigLIP2. With continued pretraining on multi-resolution images at nativeaspect ratios, GenLIP further excels at detail-sensitive tasks such as OCR,chart understanding, and visual question answering, making it a strongfoundation for vision encoders in MLLMs.

ECCV 2026

InterEdit: Navigating Text-Guided Multi-Human 3D Motion Editing

Yebin Yang, Di Wen, Lei Qi, Weitong Kong, Junwei Zheng, Ruiping Liu, Yufan Chen, Chengzhi Wu, Kailun Yang, Yuqian Fu, Danda Paudel, Luc Van Gool, Kunyu Peng

Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less ex-plored due to limited paired data and the complexity of inter-personinteractions. We introduce the task of multi-person 3D motion editing,where a target motion is generated from a source and a text instruction.To support this, we propose InterEdit3D, a new dataset with man-ual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark. We present InterEdit,a synchronized classifier-free conditional diffusion model for TMME. Itintroduces Semantic-Aware Plan Token Alignment with learnable to-kens to capture high-level interaction cues and an Interaction-AwareFrequency Token Alignment strategy using DCT and energy poolingto model periodic motion dynamics. Experiments show that InterEditimproves text-to-motion consistency and edit fidelity, achieving state-of-the-art TMME performance. The dataset and code will be released athttps://github.com/YNG916/InterEdit.

ECCV 2026

InstaEdit: Instant Image Editing via Optimized Noise Prediction

Ning Ma, Yangrui Shao

This paper proposes InstaEdit, an acceleration frameworkfor image editing models that reduces the inference steps while preserv-ing, or even improving, generation quality. Unlike existing approaches,the proposed method employed a noise prediction strategy that incorpo-rates both textual instructions and visual conditions into the noise initial-ization process, producing latent variables that are closer to intermediatestates along the generative trajectory. These optimized latent variableswere then used as the starting point for the sampling process. The keycomponent of the framework is a deep noise predictor that estimates amore suitable initial noise state for the generation procedure, therebyreducing redundant denoising steps and improving inference efficiency.InstaEdit can be seamlessly integrated into existing mainstream FlowMatching inference pipelines. We applied InstaEdit to several state-of-the-art image editing models, including Step1X-Edit, FLUX.1-Kontext,and FLUX.2-klein-base-9B. Experimental results demonstrated that In-staEdit achieved acceleration factors of 4.13×, 3.88×, and 4.22×, respec-tively, while maintaining stable and high-quality generative performanceacross multiple standard image editing benchmarks.

ECCV 2026

InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling

Tianheng Lu, Guangyu Wang, Ruqi Huang, LU FANG

Achieving truly immersive large-scale scene digitization ne-cessitates consistent and visually pleasing rendering across all possibleviewing perspectives. However, collecting multi-view images covering ev-ery fine detail of a large-scale scene is prohibitive due to scene complexity,capture cost, negligence, or accessibility constraints. As a result, the sam-pled views tend to be highly unstructured – the majority of the sceneis well covered yet certain regions inevitably lack sufficient observations.Existing reconstruction based methods are vulnerable to view scarcitywhile generation based approaches suffer from generalization, controlla-bility, and 3D consistency issues. To address this challenge, we proposeInceptionGS, which bootstraps Gaussian splatting by subtly balancingreconstruction and generation. Starting from an initial Gaussian splat-ting, InceptionGS reasonably rethinks and repairs problematic regionscaused by view scarcity while preserving the quality elsewhere, by softlyincorporating scene- and view-adaptive generative priors. Extensive ex-periments on real-world large-scale scenes demonstrate the superiorityand broad applicability of our approach in handling unstructured im-agery and boosting high-fidelity Gaussian splatting.

ECCV 2026

Histocomponent-driven Universal Model for Virtual Immunohistochemistry Multiplex Staining via Joint Manifold Evolution

Jiajun Cen, Siyuan Xu, Lili Gao, Yan Wang

Virtual immunohistochemistry (IHC) multiplex staining hasemerged as a highly promising non-destructive solution in digital pathol-ogy. However, existing universal IHC staining models typically guide thegeneration of specific biomarkers by injecting task-specific text prompts,which often struggle to bridge the semantic gap with complex patholog-ical structures, leading to the "manifold drift" problem. Moreover, thesemodels lack specificity in modeling heterogeneous tissue components.In this paper, we propose HUSE, a Histocomponent-driven Universalmodel for virtual IHC multiplex Staining via joint manifold Evolution.HUSE introduces Joint Manifold Anchoring (JMA) as a global solution totreat H&E images as persistent structural scaffolds, effectively anchoringthe generation trajectory to the intrinsic image manifold. Complementingthis, a localized refinement scheme named Histocomponent-driven Mix-ture of Experts (Hi-MoE) and Representation Conflict Gating (RCG)is introduced to precisely capture local topological features of complexmanifolds. Furthermore, we pioneer an mIF-driven multi-marker IHCgeneration paradigm to construct large-scale 1(H&E) : N (IHC) train-ing matrices; evaluations by senior pathologists verify the feasibility ofthis paradigm as well as its exceptional clinical realism and structuralfidelity. Extensive experiments on two distinct types of datasets demon-strate that HUSE achieves state-of-the-art performance. Code is releasedat https://github.com/DeepMed-Lab-ECNU/HUSE.

ECCV 2026

HERO: Heterogeneous Evidential Robust Object-Level Collaborative Perception

Hao Si, Ehsan Javanmardi, Hanlin Wu, Manabu Tsukada

Real-world collaborative perception agents typically deploydiverse sensor configurations and network architectures. While feature-level fusion methods address this heterogeneity by aligning feature spaces,they require collaborative training and large communication overhead. Incontrast, object-level fusion sidesteps these constraints, as transmittedbounding boxes are directly usable across varying architectures. How-ever, standard late fusion relies only on box coordinates and confidencescores, which provide insufficient information for robust association andleave the system vulnerable to localization noise and asynchronous de-lays. To address this, we propose HERO, an uncertainty-guided object-level collaborative perception method that equips proposals with evi-dential statistics and decouples the two fusion decisions, aggregating se-mantic evidence across matched proposals while selecting geometry fromthe most reliable source, to perform robust association and fusion underpose noise and asynchrony. Experiments on OPV2V-H and DAIR-V2Xshow that HERO matches the SOTA feature-level fusion methods whiletransmitting only about 2 KB per frame of sparse object proposals. Un-der localization error and latency scenarios, HERO further outperformsfeature-level SOTA models.

ECCV 2026

H-Adapter: Pose-Robust Hairstyle Transfer via Attention-Derived, Source-Aligned Hair Masks

Seulgi Jeong, Yunseong Cho, Sanghun Park

Hairstyle transfer has practical applications such as virtual try-on, yet remains challenging when the source and reference exhibit large head-pose discrepancies. We propose H-Adapter, which improves pose robustness by training with a region-specific loss that disentangles hair and non-hair objectives and thereby induces spatially disentangled cross-attention, from which a source-aligned hair edit mask is derived to guide diffusion-based inpainting. Experiments on pose-agnostic and posedifferent subsets demonstrate strong quantitative results, including the best FID, FID , and CLIP-I under pose differences, while maintaining CLIP competitive non-hair preservation and improving qualitative fidelity to fine-grained reference hairstyle details. Beyond source-conditioned transfer, H-Adapter supports practical extensions including reference-guided text-to-image generation, auxiliary prompt-based hair color control, and compatibility with an identity-preserving IP-Adapter variant. We also introduce a VLM-as-a-judge protocol and observe consistent gains in hairstyle faithfulness, non-hair preservation, and artifact quality.

ECCV 2026

Glance: Accelerating Diffusion Models with 1 Sample

Zhuobai Dong, Rui Zhao, songjie wu, Suyang Hou, Junchao Yi, Zhengyuan Yang, Lijuan Wang, Alex Jinpeng Wang

Diffusion models have achieved remarkable success in image generation, yet their deployment remains constrained by the heavy computational cost and the need for numerous inference steps. Previous efforts on fewer-step distillation attempt to skip redundant steps by training compact student models, yet they often suffer from heavy retraining costs and degraded generalization. In this work, we take a different perspective: we accelerate smartly, not evenly, applying smaller speedups to early semantic stages and larger ones to later redundant phases. We instantiate this phase-aware strategy with two experts that specialize in slow and fast denoising phases. Surprisingly, instead of investing massive effort in retraining student models, we find that simply equipping the base model with lightweight LoRA adapters achieves both efficient acceleration and strong generalization. We refer to these two adapters as Slow-LoRA and Fast-LoRA. Through extensive experiments, our method achieves up to 5× acceleration over the base model while maintaining comparable visual quality across diverse benchmarks. Remarkably, the LoRA experts are trained with only 1 samples on a single V100 within one hour, yet the resulting models generalize strongly on unseen prompts. Code is available at https://zhuobaidong.github.io/Glance/.

ECCV 2026

G2FM: A Geodesic Flow Matching Framework with Geometric Prior for Category-Level 9-DoF Pose Estimation

Qianyu Chen, Xiaogang Zhang, Yangyi Wan, Zhengzhao Pan, Kai Wang, Yuqi Cai, Wenbin Yan, Feng Yang, Hua Chen

Estimating category-level 9-DoF object poses (rotation, trans-lation, scale) is fundamental for robotic manipulation and autonomoussystems. Generative approaches, particularly diffusion-based models, haveachieved strong accuracy by effectively bridging the sim-to-real domaingap through large-scale synthetic training. However, it is difficult fordiffusion-based approaches to achieve both high accuracy and real-timeperformance due to their reliance on a multi-step iterative inference pro-cess. To address this limitation, we propose Geometric Prior GeodesicFlow Matching (G2FM), an efficient, simulation-free training objectivethat learns a deterministic vector field to directly map noise to pose dis-tributions. G2FM learns a vector field mapping noise to pose distribu-tions with only three ODE steps, achieving high accuracy and real-timeperformance (> 30 FPS). To avoid the structural distortion and singular-ities caused by Euclidean approximations, we construct a geometrically-consistent trajectory that operates directly on the SO(3) manifold bytransporting rotations along geodesic paths. Furthermore, to dynami-cally refine the generative flow during inference and enhance accuracywithout retraining, we design a training-free geometric guidance mech-anism by leveraging Chamfer distance gradients to project pose correc-tions directly onto the SO(3) tangent space. Extensive experiments showthat G2FM delivers a favorable accuracy-efficiency-generalization trade-off for category-level RGB-D 9-DoF pose estimation. Code will be re-leased upon publication.

ECCV 2026

FusionTrack: Collaborative Multi-Object Tracking with Arbitrary Multi-UAVs

Xiaohe Li, Pengfei Li, Kaixin Zhang, Jiahao Li, Zide Fan

Multi-object tracking (MOT) via UAV fleets is crucial for low-altitudeapplications. However, existing multi-view multi-object tracking (MVMOT) meth-ods and datasets are constrained by fixed camera configurations and limited cov-erage of complex scenarios, thereby failing to address real-world applications. Tofill this gap, we first conduct a systematic study of MOT under arbitrary camerasetups and introduce MDMOT, a novel multi-UAV benchmark covering diversereal-world scenarios, exposing practical challenges like random object entry/exitand cross-view appearance inconsistencies. We further propose FusionTrack, anend-to-end MVMOT framework that abandons the traditional decoupled track-then-associate paradigm. It jointly optimizes tracking and association via bidirec-tional object fusion between a Tracklet Memory Pool and a Trajectory IdentityPool, leveraging spatio-temporal context to enhance representation discriminabil-ity. For inference, we design View-aware Hierarchical Clustering with neighborfiltering to ensure intra-view exclusivity and inter-view consistency in cross-viewassociation. Extensive experiments verify that FusionTrack achieves state-of-the-art performance in both single- and multi-view tracking tasks, outperforming ex-isting methods by at least 2.3% in relative improvement on our MDMOT bench-mark. Project page: https://github.com/aircas501/FusionTrack

ECCV 2026

Frozen CLIP Priors for Robust Self-Supervised Poisson Inverse Problems

Laura C. Diaz-Delgado, Emmanuel Martinez, Henry Arguello

Self-supervised learning for imaging inverse problems is in-creasingly important in photon-limited settings, where acquiring cleanground truth is impractical and reconstruction must remain stable underdataset and acquisition shifts. This challenge is amplified under Poissonnoise, whose signal-dependent statistics interact with sampling operators(e.g., CFA mosaicing). Meanwhile, foundation vision encoders trained atweb scale offer distortion-invariant, content-related representations thatgeneralize well across domains, suggesting a promising route to buildpriors that transfer beyond the training distribution without expensivefine-tuning. This paper proposes an ADMM-inspired unrolled plug-and-play solver for Poisson inverse problems that decouples a closed-formdata-consistency update from a parameter-efficient prior. The prior isimplemented as a lightweight decoder operating on frozen CLIP RN50dense multi-scale features, adapting foundation representations with lesstrainable parameters. For self-supervision, the method integrates GR2Rmeasurement-domain re-corruption with an Equivariant Imaging regu-larizer via virtual acquisitions. Experiments on Poisson CFA demosaic-ing and deblurring show competitive quality, improved robustness undershifts, and self-supervised performance approaching supervised training.

ECCV 2026

From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation

Yibin Liu, Yaxing Lyu, Daqi Gao, Zhixuan Liang, Weiliang Tang, Shilong Mu, Xiaokang Yang, Mingyu Ding, Yao Mu

Accurate process supervision remains a critical challenge forlong-horizon robotic manipulation. A primary bottleneck is that currentvideo MLLMs, trained primarily under a Supervised Fine-Tuning (SFT)paradigm, function as passive “Observers” that recognize ongoing eventsrather than evaluating the current state relative to the final task goal.In this paper, we introduce PRIMO R1 (Process Reasoning InducedMOnitoring), a 7B framework that transforms video MLLMs into ac-tive “Critics”. We leverage outcome-based Reinforcement Learning to in-centivize explicit Chain-of-Thought generation for progress estimation.Furthermore, our architecture constructs a structured temporal inputby explicitly anchoring the video sequence between initial and currentstate images. Supported by the proposed PRIMO Dataset and Bench-mark, extensive experiments across diverse in-domain environments andout-of-domain real-world humanoid scenarios demonstrate that PRIMOR1 achieves state-of-the-art performance. Quantitatively, our 7B modelachieves a 50% reduction in the mean absolute error of specialized rea-soning baselines, demonstrating significant relative accuracy improve-ments over 72B-scale general MLLMs. Furthermore, PRIMO R1 exhibitsstrong zero-shot generalization on difficult failure detection tasks. Weestablish state-of-the-art performance on the RoboFail benchmark with67.0% accuracy, surpassing closed-source models like OpenAI o1 6.0%.The project website is: 10-oasis-01.github.io/primo-r1-website.

ECCV 2026

FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models

Kaitong Cai, Jusheng zhang, Sizhuo Ma, Bingqian Lu, Jian Wang, Keze Wang

Large Vision-Language Models (VLMs) typically process hun-dreds or even thousands of visual tokens per image or video frame, in-curring quadratic attention costs and significant information redundancy.Existing token-reduction methods either ignore the textual query or relyon deep attention maps, which become unstable under aggressive prun-ing and often lead to degraded semantic alignment. To address this, weintroduce FlashVLM, a text-guided visual token selection frameworkthat dynamically adapts visual inputs to the given query. Rather thanrelying on noisy attention weights, FlashVLM computes an explicit cross-modal similarity between projected image tokens and normalized textembeddings within the LLM space. It then fuses this extrinsic text-imagerelevance with intrinsic visual saliency using log-domain weighting andtemperature-controlled sharpening. Furthermore, a diversity-preservingpartitioning mechanism retains a minimal yet representative set of back-ground tokens to preserve global context. Evaluated under identical to-ken budgets and protocols, FlashVLM achieves beyond-lossless compres-sion—slightly surpassing the unpruned baseline while discarding up to77.8% of visual tokens on LLaVA-1.5. Remarkably, it maintains 92.8%performance even under an aggressive 94.4% compression rate. Extensiveexperiments across 14 image and video benchmarks demonstrate thatFlashVLM establishes a new state-of-the-art in efficiency-performancetrade-offs, exhibiting strong robustness and broad generalization acrossmainstream VLMs.

ECCV 2026

Ex-Sim(3)-Reg: 2D-3D Correspondence Pruning via Extended Sim(3) Registration

Pei An, Muyao Peng, Junfeng Ding, Jiaqi Yang, Liangliang Nan

Learning-based image-to-point-cloud (I2P) registration hasgarnered increasing attention in recent years. Nevertheless, existing meth-ods still struggle with severe outliers under challenging scenarios withunseen, low-inlier, or distorted cases. A fast and robust 2D-3D correspon-dence pruning method is therefore highly desirable. Recently, a promis-ing scheme lifts 2D-3D correspondences to 3D-3D correspondences usingdepth priors, casting correspondence pruning as a Sim(3) registrationproblem. However, depth priors estimated from monocular images areinherently noisy, which undermines the reliability of this scheme. In thispaper, to explicitly model non-negligible depth noise, we reformulatecorrespondence pruning as an extended Sim(3) registration problem andpropose a simple yet effective pruning algorithm termed Ex-Sim(3)-Reg.We further provide a theoretical analysis to justify the effectiveness ofour method. Extensive experiments on the 7-Scenes, RGBD-V2, Scan-Net, and TUM datasets demonstrate that Ex-Sim(3)-Reg achieves up to24.7% improvement in registration recall over state-of-the-art baselinemethods. Code is released at github.com/anpei96/ex-sim3-demo.

ECCV 2026

EgoSim: Egocentric World Simulator for Embodiment Interaction Generation

Jinkun Hao, Mingda Jia, Xudong Xu, Ruiyan Wang, Xihui Liu, Ran Yi, Lizhuang Ma, Jiangmiao Pang

We introduce EgoSim, a closed-loop egocentric world sim-ulator that generates spatially consistent interaction videos and per-sistently updates the underlying 3D scene state for continuous simu-lation. Existing egocentric simulators either lack explicit 3D ground-ing, causing structural drift under viewpoint changes, or treat the sceneas static, failing to update world states across multi-stage interactions.EgoSim addresses both limitations by modeling 3D scenes as updatableworld states. We generate embodiment interactions via a Geometry-action-aware Observation Simulation model, with spatial consistencyfrom an Interaction-aware State Updating module. To overcome the crit-ical data bottleneck posed by the difficulty in acquiring densely alignedscene–interaction training pairs, we design a scalable pipeline that ex-tracts static point clouds, camera trajectories, and embodiment actionsfrom in-the-wild large-scale monocular egocentric videos. Extensive ex-periments demonstrate that EgoSim significantly outperforms existingmethods in terms of visual quality, spatial consistency, and generalizationto complex scenes and in-the-wild dexterous interactions, while support-ing cross-embodiment transfer to robotic manipulation. Project page isat: egosimulator.github.io.

ECCV 2026

Efficient RGB-T Object Detection via Sparse Cross-Modality Fusion

Chao Tian, Zikun Zhou, Chao Yang, Guoqing Zhu, Zhenyu He

RGB-T detectors leverage the complementary strengths ofvisible and thermal infrared modalities, achieving robust performanceunder challenging conditions. Many of them resort to heavy dual back-bones and exhaustive cross-modality fusion across the entire image, lead-ing to impractically high computational costs. We observe that most im-age regions are smooth backgrounds (e.g., sky, ground) that can be easilyhandled by lightweight single-modality models. In light of this observa-tion, we propose a sparse fusion mechanism for efficient RGB-T detec-tion: first rapidly scanning the image to identify the proposals and thencarefully examining the remaining sparse proposals via feature fusion.We propose a two-stage framework to instantiate this mechanism, whichperforms detection in two stages: 1) a lightweight and modality-specificdetection stage that produces high-recall RoIs, and 2) a fusion-driven ex-amination and refinement stage that filters out the false positives and re-fines the bounding boxes. This design enables the detector to adaptivelyallocate more computational resources to the potential foregrounds, im-proving the efficiency while ensuring detection accuracy. Extensive ex-periments show that our method achieves competitive performance withsubstantially fewer parameters and lower cost, while maintaining strongscalability to high-resolution images.

ECCV 2026

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution

Haofei Song, Siyuan Xu, Xintian Mao, Shaojie Guo, Qingli Li, Yan Wang

Arbitrary slice super-resolution reconstructs isotropic vol-umes from anisotropic clinical acquisitions by synthesizing intermedi-ate slices at arbitrary scales. However, treating this ill-posed inverseproblem as unconstrained residual-based regression risks hallucinatinganatomically implausible structures or altering the originally observeddata. To address both concerns, this paper presents the Dual-PriorNull-Space Learning (DP-NSL) framework, which reformulates the taskas a constrained recovery process guided by two complementary priors.A Measurement-Consistent Projection (MCP) enforces a DeterministicObservation Prior : the reconstruction undergoes an exact orthogonalprojection that reproduces every acquired slice with zero error, confin-ing all learned details to the unobservable null space. Within this nullspace, a Mixture-of-Splines (MoS) module imposes a Geometric Conti-nuity Prior by dynamically mixing B-spline experts of different analyticorders, allowing each anatomical region to be modeled with a content-aware level of continuity. To promote spatial coherence, a Local SpatialConsistency Decoder (LSCD) further injects local inductive bias. Ex-periments on three CT and one MRI benchmark show that DP-NSLoutperforms existing approaches while strictly preserving measurementconsistency. Code is available at https://github.com/DeepMed-Lab-ECNU/Medical-Image-Reconstruction.

ECCV 2026

Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph

Donghyun Kim, Chanyoung Kim, YoungJoong Kwon, Seong Jae Hwang

Reconstructing building wireframe from airborne LiDAR pointclouds yields a compact, topology-centric representation that enablesstructural understanding beyond dense meshes. Yet a key limitation per-sists: conventional methods have failed to achieve accurate wireframe re-construction in regions afflicted by significant noise, sparsity, or internalcorners. This failure stems from the inability to establish an adaptivesearch space to effectively leverage the rich 3D geometry of large, sparsebuilding point clouds. In this work, we address this challenge with De-launay Canopy, which utilizes the Delaunay graph as a geometric priorto define a geometrically adaptive search space. Central to our approachis Delaunay Graph Scoring, which not only reconstructs the underlyinggeometric manifold but also yields region-wise curvature signatures torobustly guide the reconstruction. Built on this foundation, our cornerand wire selection modules leverage the Delaunay-induced prior to focuson highly probable elements, thereby shaping the search space and en-abling accurate prediction even in previously intractable regions. Exten-sive experiments on the Building3D Tallinn city and entry-level datasetsdemonstrate state-of-the-art wireframe reconstruction, delivering accu-rate predictions across diverse and complex building geometries.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.