ECCV 2026 Papers

Correspondence learning aims to estimate accurate geomet-ric model parameters from the data with severe outliers and is crucial formany computer vision tasks. While recent deep correspondence learningmethods have substantially improved inlier-outlier identification, most ofthem still rely on a differentiable weighted least squares (WLS) estima-tor to recover the final model parameters. This strategy typically treatscorrespondences as conditionally independent observations and there-fore ignores the structured dependencies among inliers. In real-worldscenarios, inliers are often sampled from geometric manifolds and tendto appear in spatially dense clusters, especially in texture-rich regions.As a result, WLS may be dominated by dense inlier groups, leadingto a poorly-conditioned estimation problem that is vulnerable to noiseor degenerate configurations. To address this bottleneck, we propose astructure-aware estimator, which explicitly models inlier correlations viaa graph Laplacian matrix and integrates this prior into a maximum-likelihood framework. Our proposed estimator is fully differentiable, ar-chitecturally lightweight, and can seamlessly replace the standard WLSestimator in existing correspondence learning pipelines. Extensive exper-iments on multiple benchmarks demonstrate consistent improvementswhile introducing a modest increase in computational cost. The code ispublicly available at: https://github.com/Tianyu-Yan/SAE

While contemporary monocular depth estimation (MDE)methods achieve remarkable overall acacy, they consistently produce er-roneous depth discontinuities at object contact regions, particularly be-tween objects and supporting surfaces. In this paper, we address this crit-ical limitation by presenting a boundary-aware monocular depth estima-tion framework that enforces depth continuity at contact areas throughthe principled exploitation of contact boundaries as explicit structuralpriors. Specifically, we propose a boundary detection and filtering modulethat explicitly identifies object contact regions, yielding a novel boundary-aware representation that enables depth-consistent learning at contactareas. Furthermore, we introduce a boundary-aware feature fusion strat-egy that seamlessly incorporates contact boundary priors into the depthdecoding process, effectively rectifying the persistent discontinuities thatelude existing approaches. Our framework further supports interactiverefinement, allowing users to manually specify missing contact bound-aries for controllable depth correction. Extensive experiments on threeunseen benchmarks with dense object interactions demonstrate the ef-fectiveness of our approach, consistently outperforming baselines withparticularly pronounced gains in contact regions. Code is available athttps://github.com/abai969/contact-depth-refinement.git

Vision–Language Models (VLMs) have recently emerged asa powerful paradigm for autonomously perceiving and interacting withcomplex and dynamic Graphical User Interface (GUI) environments.However, their interaction capability remains fundamentally limited byinsufficient precision in spatial grounding. Intriguingly, simply overlay-ing visual markers on screenshots has been extensively shown to sub-stantially enhance the grounding performance of models. Despite its em-pirical effectiveness, the underlying mechanism—specifically, why theseexplicit visual cues can influence the model’s internal spatial understand-ing—remains largely unexplained. We find that the causal influence ofvisual markers on grounding performance is mediated by low-rank la-tent subspaces associated with specific neurons in the late decoder lay-ers. Based on this key insight, we propose DeCoPatch, a novel decode-time causal intervention method that precisely modulates the activa-tions of key neurons relevant to GUI grounding. DeCoPatch follows atwo-stage design: it first identifies top-k critical neurons during the pre-fill stage, effectively isolating a low-rank latent subspace, and then re-inforces these neurons during the decoding stage to causally steer themodel’s spatial grounding behavior. Extensive experiments on four chal-lenging benchmarks demonstrate that DeCoPatch consistently enhancesthe GUI grounding performance of diverse VLMs with almost no addi-tional computational overhead.

Language-aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque, making interpreting their decision-making difficult. Recent work decompose these representations into human-interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks. In this work, we propose CFM, a language-aligned concept foundation model for vision that provides fine-grained concepts, which are human-interpretable and spatially grounded in the input image. When paired with a foundation model with strong semantic representations, we get explanations for any of its downstream tasks. Examining local co-occurrence dependencies of concepts allows us to define concept relationships through which we improve concept naming and obtain richer explanations. On benchmark data, we show that CFM provides performance on classification, segmentation, and captioning that is competitive with opaque foundation models while providing fine-grained, high quality concept-based explanations. Code at https://github.com/kawi19/CFM

3D Gaussian Splatting (3DGS) has emerged as a powerfulrepresentation for high-fidelity rendering. However, existing assets of-ten suffer from quality bottlenecks such as missing details and texturenoise. Prior attempts to enhance these assets via 2D image processingintroduce multi-view inconsistencies and high computational costs. Inthis paper, we propose a novel 3D-native refinement paradigm namedAnchorSplat. AnchorSplat is an end-to-end deep network operatingdirectly on 3D structures, avoiding the expensive optimization overheadof traditional 3D-2D-3D pipelines. Crucially, AnchorSplat is a strictlysource-free solution requiring no original multi-view images. Central tothe proposed method is the Point Anchor Mechanism, which enforcesgeometric consistency via local offset constraints, mitigating ill-posedmapping and gradient confounding. Furthermore, AnchorSplat replacesiterative densification with a single-pass multiplication mechanism. Tofacilitate research, we construct 3DGS-SR, the first large-scale bench-mark for this task. Experiments demonstrate state-of-the-art results onthe 3DGS-SR dataset, with throughput up to 105 times faster thanoptimization methods. Notably, AnchorSplat exhibits robust zero-shotgeneralization across diverse data distributions, including generativemodel outputs and real-world scans. The repository is available at: github

Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning. To bridge this gap, we present VLM-IE3D, a unified framework that enhances the 3D spatial awareness of VLMs by equipping them with both implicit and explicit 3D geometries learned from RGB videos. Our VLMIE3D introduces Implicit Geometry Tokens (IGTs) that capture high-level geometric priors from input videos, as well as complementary Explicit Geometry Tokens (EGTs) that encode detailed geometric structures from reconstructed 3D attributes. On top of that, VLM-IE3D comes with a 3D-aware adapter that effectively fuses the two types of geometric representations with 2D visual cues. This RGB-only design injects strong 3D inductive biases for fine-grained spatial understanding and reasoning without requiring any additional 3D inputs. Extensive experiments show that VLM-IE3D achieves superior performance consistently across various 3D tasks including 3D video detection, 3D visual grounding, 3D dense captioning, and spatial reasoning. Code and models are available at https://github.com/Vegetebird/VLM-IE3D.

Efficient strategies to jointly adapt and deploy large language models have seen a growing need under resource-limited conditions for downstream applications. However, when applied to visual foundation models, existing methods typically incur either high GPU memory consumption during adaptation or extra computation costs introduced by the adapters at deployment. In this paper, we propose Efficient Quantization-aware Adaptation (EQuA) that achieves high efficiency in both adaptation and deployment for visual foundation models. We observe that dominant memory consumption arises from intermediate activations cached for backpropagation in the deep backbone and activation quantizers. To address this issue, we split a lightweight sub-network from the backbone during adaptation as a side adapter branch, and tailor two adaptation strategies to eliminate these cached activations, thereby significantly reducing memory consumption. At deployment, the side adapter branch is merged back into the backbone, yielding a quantized model without any extra computation costs. Extensive experiments on representative visual foundation models and diverse downstream tasks exhibit that EQuA achieves an elegant trade-off between performance and efficiency. For example, EQuA yields over 70% GPU memory reduction compared to state-of-the-art baselines while maintaining competitive performance. Code: https://github.com/leenas233/EQuA.

Attention sinks are defined as tokens that attract dispro-portionate attention. While these have been studied in single modalitytransformers, their cross-modal impact in Large Vision-Language Mod-els (LVLM) remains largely unexplored: are they redundant artifactsor essential global priors? This paper first categorizes visual sinks intotwo distinct categories: ViT-emerged sinks (V-sinks), which propagatefrom the vision encoder, and LLM-emerged sinks (L-sinks), which arisewithin deep LLM layers. Based on the new definition, our analysis re-veals a fundamental performance trade-off: while sinks effectively encodeglobal scene-level priors, their dominance can suppress the fine-grainedvisual evidence required for local perception. Furthermore, we identifyspecific functional layers where modulating these sinks most significantlyimpacts downstream performance. To leverage these insights, we proposeLayer-wise Sink Gating (LSG), a lightweight, plug-and-play module thatdynamically scales the attention contributions of V-sink and the restvisual tokens. LSG is trained via standard next-token prediction, re-quiring no task-specific supervision while keeping the LVLM backbonefrozen. In most layers, LSG yields improvements on representative mul-timodal benchmarks, effectively balancing global reasoning and preciselocal evidence.1

Text-driven hand–object interaction (HOI) generation is gain-ing attention for immersive applications and robotics, yet producingphysically plausible interactions remains challenging. Even when individ-ual motions appear natural, small contact errors can cause conspicuousartifacts such as floating and interpenetration. Prior methods mitigatethese issues using explicit contact cues or implicit grasp priors, but typ-ically rely on multi-stage pipelines and fail to model temporally evolvingcontact. We present JointHOI, a single-stage diffusion framework thatjointly generates 3D hand–object motion and dynamic, distance-basedcontact maps from text. By treating contact as an auxiliary inner modal-ity, joint generation enables the model to learn contact–motion couplingduring training. At inference, contact-guided sampling enforces consis-tency between generated contact maps and motion-implied geometry,improving temporal stability and reducing penetration and floating. Ex-periments on GRAB and ARCTIC demonstrate consistent improvementsin text adherence and physical plausibility over prior methods.

Higher-order ODE solvers are widely believed to improvesample quality in diffusion and flow-based generative models at a fixednumber of function evaluations (NFE). We show that this assumptioncan fail: at practical step sizes, higher-order methods can systematicallyunderperform first-order Euler. The mechanism is a multiplicative in-teraction between discretization and learning error. Applying backwarderror analysis to a learned field fˆ = f +ε reveals interaction terms hk ∇k εthat dominate classical truncation error at practical NFEs in diffusionand flow models, causing a solver of nominal order p to behave as lowerorder—pre-asymptotic order collapse. Our analysis yields a predictivecrossover step size h⋆ characterizing when higher order helps or hurts.Validation on toy manifolds and pretrained diffusion and flow modelsconfirms the predicted hierarchy: Euler is best at low NFE, Heun over-takes at moderate budgets, and RK4 can surpass Heun at higher NFE.Building on this theory, we propose an adaptive sampler that dynami-cally selects solver order based on the local roughness score R = h/h⋆ ,estimated at zero extra NFE, and often improves sample quality acrossmodels and datasets.

Underwater computer vision tasks, such as detection, restora-tion, and segmentation, are limited by the scarcity of large-scale and di-verse training data. We introduce WaterGen, a method for generatinglarge-scale, realistic, and diverse underwater images that provides inde-pendent control of the scene and water medium conditions. Our approachtreats underwater image generation as the decoupled control of two fac-tors: realistic and diverse scene content (what is in the image), and accu-rate and controllable water medium effects (what the water does to theimage). Existing methods generally achieve only part of this objective:they either provide controllability with limited realism or diversity, orgenerate realistic scenes without accurately and independently modelingwater-medium effects. Our key insight, that allows us to avoid this com-promise, is that scene generation and medium modeling can be decoupledwithin a latent diffusion framework, enabling diverse scene generation to-gether with accurate and controllable underwater appearance. To do this,we decompose underwater image synthesis into two stages. First, we fine-tune the latent diffusion U-Net using degradation-free underwater imagesso that it learns to generate diverse and realistic latent embeddings ofunderwater scene content without medium-induced degradation. Second,we formulate the physically accurate medium degradation synthesis asa conditional decoding process applied to these latent embeddings. Thisdecoupled design allows our model to generate diverse scenes with fullcontrol of underwater appearance. We leverage WaterGen to build large-scale synthetic underwater datasets that are diverse in scene structuresand accurate in water effects and pseudo-labels. We demonstrate that oursynthetic data consistently improve downstream performance in under-water restoration and semantic segmentation. Code and model weightsare available at https://github.com/jiayi-wu-umd/WaterGen.

Extracting structured, parametric 3D representations fromraw images remains a fundamental challenge in computer vision andgraphics. While recent advancements in the 3D Gaussian Splatting (3DGS)pipeline integrate planar primitives to yield compact and editable geom-etry, these approaches typically treat planes as isolated, discrete sets.This lack of topological connectivity hinders robust geometric reasoning,leading to fragmented reconstructions and misaligned boundaries thatfall short of the precision for rigorous spatial analysis and professionaldesign workflows. To address this, we introduce TopoGS, the first 3DGSframework to explicitly integrate both planar and topological constraintsfor coherent 3D reconstruction. Specifically, we extract global 2D topo-logical relationships from multi-view image segmentations and anchorGaussian primitives to these structural elements. This formulation en-ables the joint optimization of plane parameters, rendering fidelity, andtopological adjacency. By enforcing strict multi-view consistency along-side these topological constraints, our method significantly mitigates ge-ometric misalignments and produces connected, structured 3D models.Extensive evaluations on the ScanNet++ dataset demonstrate that To-poGS achieves state-of-the-art performance, providing a highly robustsolution for generating accurate, topologically sound, and visually faith-ful scene representations.

Reliable 3D dynamic perception requires models that can anticipatemotion beyond predefined categories, yet progress is hindered by the scarcityof dense, high-quality motion annotations. While self-supervision on unlabeledreal data offers a path forward, empirical evidence suggests that scaling unla-beled data fails to close the performance gap due to noisy proxy signals. In thispaper, we propose learning robust real-world motion priors entirely from scal-able simulation. We introduce SynFlow, a data generation pipeline for large-scalesynthetic LiDAR scene flow. Unlike prior works that prioritize sensor-specificrealism, SynFlow employs a motion-oriented strategy to synthesize diverse kine-matic patterns across 4,000 sequences (∼940k frames), termed SynFlow-4k. Thisrepresents a 34× scale-up in annotated volume over existing real-world bench-marks. Our experiments demonstrate that SynFlow-4k provides a highly domain-invariant motion prior. In a zero-shot regime, models trained only on our syntheticdata generalize across multiple real-world benchmarks, comparable to in-domainsupervised baselines on nuScenes and outperforming state-of-the-art methodson TruckScenes by 31.8%. Furthermore, SynFlow-4k serves as a label-efficientfoundation: fine-tuning with only 5% of real-world labels surpasses models trainedfrom scratch on the full available budget. We open-source the pipeline and datasetto facilitate research in generalizable 3D motion estimation. More detail can befound at https://kin-zhang.github.io/SynFlow.

Imitation Learning (IL) offers an effective approach for robotmanipulation by learning a mapping from visual inputs to actions. How-ever, this paradigm suffers from poor robustness under visual domainshifts (e.g., lighting conditions, camera viewpoints, etc.), often failingto perform manipulation. Our key insight in tackling this problem is toconstruct a Structure Map encoding the fine-grained object structuresthat remain invariant across visual domains and vital for manipulation.Based on this insight, we propose StructPolicy, a method elaboratelydesigned to incorporate the domain-invariant Structure Map into the ILpolicy through two modules: StructCon and StructEncoder. StructCon isan automated Structure Map construction module that leverages struc-ture primitives to enable flexible transformation and composition to forma wide range of objects. StructEncoder is a hierarchical network thatefficiently captures structural relationships and affordance from Struc-ture Map. This provides the IL policy with both domain-invariant andstructurally-aware features, guiding it toward robust manipulation un-der visual domain shifts. We extensively evaluate StructPolicy across 49manipulation tasks in multiple benchmarks and diverse real-world tasksunder various visual changes. The results demonstrate consistent andsignificant performance improvements across all tasks, validating thatStructPolicy enhances the effectiveness and robustness against visual do-main shifts of the IL policy, improving manipulation accuracy.

Generating complete 3D scenes from sparse, unconstrainedviews is a fundamental challenge in 3D vision which requires reason-ing beyond observed content while remaining computationally tractable.Existing feed-forward reconstruction methods are inherently limited tocontent visible in the input images, while 3D generative modeling is hin-dered by the high computational cost of dense volumetric representationsand the scarcity of large-scale 3D supervision. We introduce SPAR3S, asparse voxel-aligned 3D latent generative model for conditional scenecompletion without requiring ground-truth 3D data for supervision. Ourkey insight is to formulate 3D scene generation in a structured, compact,voxel-aligned 3D latent space where only occupied voxels are represented.We learn this sparse latent space directly from multi-view images usingphotometric supervision via differentiable 3D Gaussian Splatting. Givena partial set of observed voxels encoded from sparse input views, scenecompletion reduces to predicting the missing latent tokens and theirspatial support within the voxel grid. To this end, we train a maskedautoregressive transformer that jointly models voxel occupancy and la-tent token values, enabling efficient and spatially consistent generation ofunseen regions. We demonstrate the effectiveness of our method on syn-thetic indoor scenes, achieving higher novel-view quality than prior work.We further validate its generalization on RealEstate10k, highlighting itsapplicability to real-world data.

Video Temporal Grounding (VTG) aims to localize momentsin untrimmed videos based on language queries. A fundamental chal-lenge lies in learning cross-modal representations that capture bothsemantic correspondence and temporal specificity. Existing approachespredominantly rely on token-level visual-textual cross-attention, whichmay not fully capture two essential properties: generalizability acrossvideos and distinguishability within videos. In practice, semanticallyrelated vocabularies may correspond to similar visual concepts, whiletemporally sparse yet crucial semantics can be easily diluted by fre-quently occurring visual patterns. To address these challenges, we pro-pose Semantic-Explicit Detection Transformer (SE-DETR), a unifiedframework that explicitly models semantic structures for video-languagegrounding. We introduce a Semantics-Proxied Alignment (SPA) mod-ule that learns semantic proxies dynamically optimized during training,and enables concept-level alignment between multimodal representations.Furthermore, we design a Temporal Sparsity Modulation (TSM) mod-ule that estimates the temporal sparsity of semantic components anddynamically reweights word-guided visual features to highlight informa-tive concepts for accurate moment localization. Extensive experimentson five benchmarks demonstrate that SE-DETR consistently achievescompetitive performance across Video Moment Retrieval, Highlight De-tection, and Video Summarization tasks. The source code is avaliable at:https://github.com/hu-cheng-yang/ECCV26-SE-DETR.

Real-time inference of vision–language–action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for accelerating inference, most existing methods mainly base pruning decisions on shallow-layer cues and risk discarding visual information required by deep layers. To address this issue, we propose SAFE-Pruner, a plug-and-play pruning framework that incorporates attention cues of future layers into pruning decisions. Specifically, we identify semantic attention consistency, the tendency that VLA models concentrate their attention probability mass on the same semantic entity across control timesteps. Based on this observation, we design a forwardlooking strategy to forecast the token saliency in deep layers, which prevents the premature removal of critical tokens and leads to more stable acceleration. We further introduce a reference timestep refresh strategy that triggers updates upon attention shifts, thereby improving forecasting accuracy and pruning reliability. Extensive experiments across diverse evaluation settings demonstrate that our method achieves up to 1.89× speedup with a minimal degradation in success rate of less than 1.5%, while outperforming state-of-the-art methods by up to 1.9%.

Multi-perturbation adversarial training (MAT) aims to achieverobustness against multiple ℓp perturbations but suffers from robustnesstrade-offs between different threats. To address this, we employ a mixtureof experts (MoE) to route different threats through distinct model path-ways. However, naïve application of MoE encounters two critical chal-lenges: experts tend to overlook threat-specific features and redundantlycapture features shared across threats, and gating networks suffer fromthreat-agnostic routing where they learn nearly identical routing pat-terns across threats, thus preventing the construction of threat-specificmodel pathways. To this end, we propose Robust Mixture of Low-RankExperts (RoME), where each expert is a low-rank additive update tothe shared backbone, allowing it to capture threat-common featureswhile experts focus on threat-specific information. To address threat-agnostic routing, RoME introduces (i) dual-scale gating that exploitsthreat-discriminative signals from local and global level features, and(ii) threat-guided gating diversification that enforces diverse expert uti-lization across threats. Extensive experiments demonstrate that RoMEoutperforms existing state-of-the-art MAT in union robustness and nat-ural accuracy and improves robustness against unseen threats. Codes areavailable at https://github.com/wkim97/RoME.

In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of trackingaccuracy. We argue that, compared to solely pursuing a perfect scenerepresentation, it is more critical to enhance the robustness of the ras-terization process against parameter errors to ensure stable camera posetracking. To address this challenge, we propose a novel approach thatleverages a smooth kernel strategy to enhance the robustness of 3DGS-based SLAM. Unlike conventional methods that focus solely on minimiz-ing rendering error, our core insight is to make the rasterization processmore resilient to imperfections in the 3DGS parameters. We hypothesizethat by allowing each Gaussian to influence a smoother, wider distribu-tion of pixels during rendering, we can mitigate the detrimental effectsof parameter noise from outlier Gaussians. This approach intentionallyintroduces a controlled blur to the rendered image, which acts as a reg-ularization term, stabilizing the subsequent pose optimization. While acomplete redesign of the rasterization pipeline is an ideal solution, wepropose a practical and effective alternative that is readily integratedinto existing 3DGS frameworks. Our method, termed Corrective BlurryKNN (CB-KNN), adaptively modifies the RGB values and locations ofthe K-nearest neighboring Gaussians within a local region. This dynamicadjustment generates a smoother local rendering, reducing the impactof erroneous GS parameters on the overall image. Experimental resultsdemonstrate that our approach, while maintaining the overall quality ofthe scene reconstruction (mapping), significantly improves the robust-ness and accuracy of camera pose tracking.https://github.com/xju-zsh/Robust-3DGS-based-SLAM-via-Adaptive-Kernel-Smoothing.git