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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

Contrastive Conditional–Unconditional Alignment for Long-tailed Diffusion Model

Fang Chen, Alex Villa, Gongbo Liang, Li Fuxin, Xiaoyi Lu, Meng Tang

Training data for class-conditional image synthesis often ex-hibit a long-tailed distribution with limited amount of images for tailclasses. Such an imbalance causes mode collapse and reduces the diver-sity of synthesized images for tail classes. For class-conditional di!usionmodels trained with imbalanced data, we aim to improve the diversityand fidelity of tail class images without compromising the quality of headclass images. We propose contrastive conditional-unconditional align-ment (CCUA), which comprises two synergistic loss functions. Our firstloss is an Alignment Loss (AL) that aligns class-conditional generationwith unconditional generation at large timesteps. Alignment loss makesthe denoising process insensitive to class conditions for the initial steps,which enriches tail classes through knowledge sharing from head classes.Secondly, we diversify unconditional generation via an UnsupervisedContrastive Loss (UCL) to increase the distance/dissimilarity amongsynthetic images. We combine the two losses to implicitly diversify condi-tional generation. Our framework is easy to implement as demonstratedon both U-Net based architecture and Di!usion Transformer. Our methodoutperforms vanilla denoising di!usion probabilistic models, score-baseddi!usion model, and alternative contrastive methods for class-imbalancedimage generation across various datasets, in particular ImageNet-LT with256→256 resolution.

ECCV 2026

ConTrack: Constrained Hand Motion Tracking with Adaptive Trade-off Control

Yutong Liang, Quanquan Peng, Rizhao Qiu, Xiaolong Wang

Human demonstrations provide strong priors for robot ma-nipulation, yet it is non-trivial to transfer them to execute on real robotsdue to the kinematic gap. In dexterous manipulation, it remains chal-lenging to track long-horizon, contact-rich sequences even in simulators:a reference-tracking policy must keep objects on their target trajectorieswhile preserving demonstrated joint motion and contact timing. Exist-ing approaches often rely on hand-crafted reward tuning that requireper-sequence tuning and break under limited interaction budgets. We in-troduce ConTrack, a reinforcement learning (RL) framework that scaleswith tracking data. ConTrack treats object tracking as a constraint andallocates remaining control authority to motion fidelity, which allows itto adapt task–style trade-offs online using a dual-variable update. In ad-dition, ConTrack also stabilizes long-horizon learning with an adaptivemid-trajectory reset library that reuses policy-reachable simulator states.Our qualitative and quantitative results in simulation tracking and realrobot demonstrate that ConTrack improves success and object pose ac-curacy significantly over prior arts while preserving joint and contactfidelity. Website: https://www.lyt0112.com/projects/ConTrack.

ECCV 2026

CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection

Junhao Cai, Deyu Zeng, Junhao Pang, Junyu Chen, Qiwei Liang, Xiaopin Zhong, Zongze Wu

Few-shot anomaly detection remains challenging due to lim-ited training data. Multi-modal anomaly detection (MAD) offers a viablesolution, leveraging 3D geometric cues to enrich 2D RGB representationsand compensate for this scarcity. However, existing MAD methods applyspatially uniform feature processing, conflating stable macroscopic struc-tures with high-frequency localized defect signals, exacerbating cross-modal misalignment and inflating false-positive rates. To overcome this,we present CMDS-AD, a Cross-Modal Dual-Stream Anomaly Detectionframework. A LoRA-guided diffusion model generates diverse RGB sam-ples to mitigate extreme data scarcity. For 3D normal augmentation, weemploy a pre-trained diffusion model as a normal estimator. Crucially,this estimator inherently acts as a non-linear low-pass filter, directly ex-tracting low-frequency normal representations from RGB inputs. Thisestablishes an auxiliary estimated stream of purely low-frequency infor-mation, anchoring robust structural templates and assisting the uncom-pressed real stream, containing coupled high- and low-frequency compo-nents, to precisely isolate micro-defects. A Coordinate-Aware Hierarchi-cal Feature Mapper adaptively aligns cross-modal semantics, while a mul-tiplicative scoring mechanism filters modality-specific noise. Under theextreme 1-shot setting, CMDS-AD achieves absolute performance gainsof 5.7% (I-AUROC) and 2.0% (AUPRO) on MVTec 3D-AD, along-side 7.7% and 5.6% improvements on EyeCandies, establishing a newstate-of-the-art. Code is available at Junhaocai27/CMDS-AD.

ECCV 2026

Beyond Categorical Matching: Intra-Class Graded Relevance Estimation for Cross-Modal 3D Retrieval

Shunning Liu, Yingpeng Zhang, Jianing Lin, Yifan Wang, Yang Zhang, Chun Yuan

Mainstream 3D asset retrieval relies predominantly on category-level matching, overlooking intra-class variations in geometry, style, andtexture critical for real-world applications. To address this, we redefineretrieval from binary categorical matching to intra-class graded relevanceestimation, treating relevance as a continuous variable to capture fine-grained semantic nuances. We propose a tri-modal encoder incorporatinga Semantic Conditioned Interaction module for cross-modal fusion and aQuery-Guided Mixture-of-Experts module for dynamic modality weight-ing. This design explicitly decouples intra-class variations and preventssemantic collapse via a re-parameterization mechanism. Furthermore,we introduce an automated, scalable benchmark generation frameworkdriven by a Multimodal Large Language Model to synthesize high-qualitysoft relevance labels, proposing a new benchmark: INGRE. Extensive ex-periments demonstrate our method achieves state-of-the-art results onINGRE while maintaining superior performance and robustness on stan-dard classification tasks.

ECCV 2026

BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models

Vishal Narnaware, Ashmal Vayani, Rohit Gupta, Swetha Sirnam, Shah Mubarak

Stereotype biases in Large Multimodal Models (LMMs) per-petuate harmful societal prejudices, undermining the fairness and equityof AI applications. As LMMs grow increasingly influential, addressingand mitigating inherent biases related to stereotypes, harmful generations,and ambiguous assumptions in real-world scenarios has become essential.However, existing datasets evaluating stereotype biases in LMMs oftenlack diversity, rely on synthetic images, and often have single-actor im-ages, leaving a gap in bias evaluation for real-world visual contexts. Toaddress the gap in bias evaluation using real images, we introduce theBBQ-Vision (BBQ-V), the most comprehensive framework for assessingstereotype biases across nine diverse categories and 50 sub-categories withreal and multi-actor images. BBQ-V benchmark contains 14,144 image-question pairs and rigorously evaluates LMMs through carefully curated,visually grounded scenarios, challenging them to reason accurately aboutvisual stereotypes. It offers a robust evaluation framework featuring real-world visual samples, image variations, and open-ended question formats.BBQ-V enables a precise and nuanced assessment of a model’s reasoningcapabilities across varying levels of difficulty. Through rigorous testingof 19 state-of-the-art open-source (general-purpose and reasoning) andclosed-source LMMs, we highlight that these top-performing models areoften biased on several social stereotypes, and demonstrate that the think-ing models induce more bias in the reasoning chains. This benchmarkrepresents a significant step toward fostering fairness in AI systems andreducing harmful biases, laying the groundwork for equitable and sociallyresponsible LMMs. Dataset and evaluation code are available here.

ECCV 2026

AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels

Javier Tirado-Garín, Alan Savio Paul, Shuai Chen, Axel Barroso Laguna, Tommaso Cavallari, Daniyar Turmukhambetov, Victor Adrian Prisacariu, Eric Brachmann

Neural map matchers estimate an image’s 3-DoF pose rela-tive to a 2D map. These models are trained on large-scale datasets ofgeo-referenced images, whose position and heading labels often containnoise that affects the trained models. To address this, we present Auto-Compass, a supervision approach for training neural map matchers frominaccurate absolute pose labels. First, we show that heading labels areunnecessary: trained from raw GPS labels, models learn to predict accu-rate headings, automatically. Second, defining a tolerance region aroundraw GPS improves positional accuracy. Third, if available, our supervi-sion uses relative poses between training images, obtained via SLAM orSfM, which provide a more accurate training signal. Across driving andegocentric benchmarks, AutoCompass consistently outperforms counter-parts trained with the usual strong reliance on absolute pose labels.

ECCV 2026

AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generation

Xuanhua Yin, Chuanzhi Xu, Haoxian Zhou, Boyu Wei, Weidong Cai

Diffusion Transformers (DiTs) are a dominant backbone forhigh-fidelity text-to-image generation due to strong scalability and align-ment at high resolutions. However, quadratic self-attention over densespatial tokens leads to high inference latency and limits deployment. Weobserve that denoising is spatially non-uniform with respect to aestheticdescriptors in the prompt. Regions associated with aesthetic tokens re-ceive concentrated cross-attention and show larger temporal variation,while low-affinity regions evolve smoothly with redundant computation.Based on this insight, we propose AccelAes, a training-free frameworkthat accelerates DiTs through aesthetics-aware spatio-temporal reduc-tion while improving perceptual aesthetics. AccelAes builds AesMask, aone-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparsereallocates computation and guidance to masked regions. We further re-duce temporal redundancy using a lightweight step-level prediction cachethat periodically replaces full Transformer evaluations. Experiments onrepresentative DiT families show consistent acceleration and improvedaesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11×speedup and improves ImageReward by +11.9% over the dense baseline.Code is available at https://github.com/xuanhuayin/AccelAes.

ECCV 2026

Zero-Shot Novel Depth Synthesis Using Foundation Models Scene Representations

Denis Akola, David Fouhey

3D Foundation Models (3DFMs) such as VGGT have re-cently pushed the boundaries of 3D vision by predicting rich unifiedrepresentations with feed-foward transformers. The scene representationslearned by these models enable strong performance on multiple 3D visiontasks. In this paper, we investigate using their internal representationsto infer 3D in the scene from new views. Our hypothesis is that in or-der to solve the task of 3D reconstruction, these models need to learn arepresentation that includes a large amount of general knowledge about3D scenes. After showing that it is possible to decode hidden surfacesfrom internal 3DFM representations, we propose a method, Z3D, thatestimates pointmaps in unseen views by doing latent diffusion on 3DFMrepresentation. We show that Z3D can predict realistic depthmaps fornew views across multiple datasets.

ECCV 2026

XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity

Junwen Huang, Jiaqi Hu, Peter Yu, Slobodan Ilic, Martin Sundermeyer, Benjamin Busam

While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial environments. We introduce XYZ-IBD, a high-precision benchmark for object detection and 6D pose estimation specifically designed for industrial bin-picking. XYZ-IBD addresses the domain gap by providing 75 multi-view real-world scenes containing approximately 273k annotated instances of metallic, symmetrical, and specular objects. Unlike existing datasets, our benchmark features high-density stochastic stacking and multi-instance ambiguity, reflecting authentic robotic manipulation challenges. We employ a rigorous multistage and semi-automatic annotation pipeline, ensuring sub-millimeter annotation accuracy. The annotations are validated through our designed error quantification scheme, securing the reliability of the annotation quality. In addition to real-world evaluation data, we provide a large-scale complementary synthetic training set that is rendered under realistic bin-picking simulation. Benchmarking state-of-the-art (SOTA) methods for 2D detection and 6D pose estimation reveals a significant performance degradation compared to standard household benchmarks, highlighting the unsolved challenges of industrial vision. XYZ-IBD establishes a new frontier for robust pose estimation in complex, high-occlusion, and reflective scenarios. The dataset and benchmark are publicly available at https://xyz-ibd.github.io.

ECCV 2026

DH-VLM: Dual-Horizon Cooperative Latent Reasoning for Autonomous Driving

Ziyi Song, Chen Xia, Hang Yu, Sheng Zhou, Zhisheng Niu

Large-scale language models for autonomous driving enableenhanced global understanding and long-horizon planning. However, whendeployed in isolated vehicles, limited sensing range and occlusions re-strict reliable decision-making, and the substantial computational andlatency overhead makes on-board deployment impractical. Cooperativedriving provides a potential solution by leveraging external agents forinformation exchange, but existing methods remain limited in semanticreasoning capability under practical constraints. To address these chal-lenges, we propose DH-VLM, a dual-horizon cooperative latent reasoningframework that enables asymmetric semantic cooperation between theinfrastructure and ego vehicle. The infrastructure aggregates multi-layerhidden states to form a global-reasoning horizon latent guidance, whichis integrated into the ego model through an Infrastructure-Driven LatentEvolution mechanism for conditional latent refinement. This enables theego vehicle to leverage long-range contextual understanding while pre-serving autonomous decision-making within its local planning horizon.Furthermore, we construct a cooperation-oriented question–answer (QA)dataset covering fundamental scene understanding and ego-personalizedcomprehension to support counterfactual and safety-aware reasoning.Extensive experiments demonstrate that DH-VLM achieves state-of-the-art planning performance, outperforming the previous state of the art by14.6% in L2 error and 26.9% in collision rate. Compared with query-basedend-to-end cooperative driving methods, our approach reduces the com-munication cost by 57.3% and GPU memory usage by 25.5%, while main-taining strong robustness against infrastructure guidance errors, provid-ing a practical and robust paradigm for cooperative autonomous driving.

ECCV 2026

WorldCache: Content-Aware Caching for Accelerated Video World Models

Umair Nawaz, Ahmed Heakl, Ufaq Khan, ABDELRAHMAN YOUSSIEF, Salman Khan, Fahad Shahbaz Khan

Diffusion Transformers (DiTs) power high-fidelity video worldmodels but remain computationally expensive due to sequential denois-ing and costly spatio-temporal attention. Training-free feature cachingaccelerates inference by reusing intermediate activations across denois-ing steps; however, existing methods largely rely on a Zero-Order Holdassumption i.e., reusing cached features as static snapshots when globaldrift is small. This often leads to ghosting artifacts, blur, and motion in-consistencies in dynamic scenes. We propose WorldCache, a Perception-Constrained Dynamical Caching framework that improves both whenand how to reuse features. WorldCache introduces motion-adaptive thresh-olds, saliency-weighted drift estimation, optimal approximation via blend-ing and warping, and phase-aware threshold scheduling across diffusionsteps. Our cohesive approach enables adaptive, motion-consistent featurereuse without retraining. On Cosmos-Predict2.5-2B evaluated on PAI-Bench, WorldCache achieves 2.3× inference speedup while preserving99.4% of baseline quality, substantially outperforming prior training-free caching approaches.

ECCV 2026

SAEdit: Token-Level Control for Continuous Image Editing via Sparse Autoencoder

Ronen Kamenetsky, Sara Dorfman, Daniel Garibi*, Roni Paiss, Or Patashnik, Danny Cohen-Or

Large-scale text-to-image di_x001B_usion models have become thebackbone of modern image editing, yet text prompts alone do not o_x001B_eradequate control over the editing process. Two properties are especiallydesirable: disentanglement, where changing one attribute does not un-intentionally alter others, and continuous control, where the strengthof an edit can be smoothly adjusted. We introduce a method for dis-entangled and continuous editing through token-level manipulation oftext embeddings. The edits are applied by manipulating the embeddingsalong carefully chosen directions, which control the strength of the tar-get attribute. To identify such directions, we employ a Sparse Autoen-coder (SAE), whose sparse latent space exposes semantically isolateddimensions. Our method operates directly on text embeddings withoutmodifying the di_x001B_usion process, making it model agnostic and broadlyapplicable to various image synthesis backbones. Experiments show thatit enables intuitive and e_x001E_cient manipulations with continuous controlacross diverse attributes and domains.

ECCV 2026

CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics

Ziyi Ding, Xianxin Lai, Weiyu Chen, Xiao-Ping Zhang, Jiayu Chen

Latent world models roll observations forward accurately onfactual transitions, but their predictive latents are typically entangledand weakly aligned with the underlying causal factors; as a result, theydegrade under interventions and on counterfactual queries. We addressthis with CausalVAE, a plug-in structural module that attaches to di-verse encoder–transition backbones and organizes their latents througha learned directed acyclic graph (DAG) over causal factors, withoutaltering the base prediction architecture. Across four benchmarks andeight backbones, the plug-in largely preserves factual retrieval while im-proving intervention-aware counterfactual retrieval on most backbone–benchmark pairs; the gains are benchmark- and backbone-dependentrather than universal. Improvements are largest on the Physics bench-mark: for a graph-network backbone trained with a negative-log-likelihoodobjective, counterfactual H@1 (CF-H@1) rises from 11.0 to 41.0, withseveral other backbones gaining +10 to +30 CF-H@1 points. A causalanalysis further shows that the learned structure recovers meaningfulfirst-order physical interaction trends, supporting the interpretability ofthe latent causal structure.

ECCV 2026

Twin-DAgger: Synergizing Digital Twins and Human Corrections for Efficient Robot Manipulation

Jiahang Li, Zhirui Zhang, Kairan Ding, Fan Fei, Yunkai Tang, Jiaming Liu, Xiao He, Ziyu Chen, Gaohao Zhou, Jieji Ren, Yandong Guo, Shanghang Zhang, Boxin Shi

Imitation learning enables robots to acquire complex manipulation skills from expert demonstrations, but offline-trained policies struggle to generalize beyond their training distribution, where small errors compound into severe distribution shift. Dataset Aggregation (DAgger) mitigates this by collecting human corrections during deployment, yet conventional DAgger operates entirely in the real world with a oneto-one ratio between expert effort and training trajectories makes comprehensive out-of-distribution (OOD) coverage impractical. Digital Twin (DT) techniques can scale data generation, but when applied to DAgger data without modification, they ignore the spatial and temporal failure patterns that DAgger rollouts reveal, leaving policies vulnerable precisely where and when they fail most. We propose Twin-DAgger, which treats each real-world intervention as simultaneously a spatial probe identifying failure-prone workspace regions and a temporal prior marking critical deviation moments. Twin-DAgger exploits these signals through two targeted augmentation mechanisms: difficulty-based stratified sampling concentrates synthetic data generation on high-failure-rate regions, and intervention-focused trajectory warping perturbs trajectories at each intervention onset to simulate a distribution of near-failure states while preserving the expert’s corrective recovery. On real-world manipulation tasks, Twin-DAgger matches conventional DAgger performance using only 15% human interventions and outperforms failure-agnostic DT baselines by 20% success rate under equal data budgets, demonstrating that generating data where and when the policy fails, rather than generating more data uniformly, is the critical driver of sample-efficient robustness.

ECCV 2026

Vision-TTT: Efficient and Expressive Visual Representation Learning with Test-Time Training

Quan Kong, Yanru Xiao, Yuhao Shen, Cong Wang

Learning efficient and expressive visual representation haslong been the pursuit of computer vision research. While Vision Trans-formers (ViTs) gradually replace traditional Convolutional Neural Net-works (CNNs) as more scalable vision learners, their applications areplagued by the quadratic complexity of the self-attention mechanism. Toaddress the challenge, we introduce a new linear-time sequence modelingmethod Test-Time Training (TTT) into vision and propose Vision-TTT,which treats visual sequences as datasets and compresses the visual to-ken sequences in a novel self-supervised learning manner. By incorporat-ing the dual-dataset strategy and Conv2d-based dataset preprocessing,Vision-TTT effectively extends vanilla TTT to model 2D visual cor-relations with global receptive fields. Extensive experiments show thatVittt-T/S/B achieve 77.7%, 81.8%, 82.7% Top-1 accuracy on ImageNetclassification and also greatly outperform their counterparts on down-stream tasks. At 1280 × 1280 resolution, Vittt-T reduces FLOPs by79.4% and runs 4.72× faster with 88.9% less memory than DeiT-T.These results demonstrate the expressiveness and efficiency of Vision-TTT as an alternative for the next-generation visual backbone. Codesare available at https://github.com/imKQv/Vision-TTT.

ECCV 2026

Unordered Landmark Visual Navigation

Hao Ren, Junzhe Zhu, Yihan Li, Zetong Bi, Le Zheng, Zhi Li, Yiqing Yuan, Zhaoliang Wan, Dizhe Zhang, Lu Qi, Hui Cheng

Image-goal navigation is a fundamental capability for em-bodied AI, yet its practical deployment is strained by strong prior as-sumptions. Existing methods predominantly rely on temporally orderedvideo streams or auxiliary sensors (e.g., depth, LiDAR) to maintain spa-tial consistency. These sequential and multimodal dependencies severelyrestrict scalability, especially when deploying robots using crowd-sourcedor pre-recorded unordered image collections. When temporal priors areremoved, current methods struggle with severe perceptual aliasing, noisyassociations, and catastrophic mapping failures. To address this under-explored challenge, we propose Unordered Landmark Visual Navigation(ULVN), a unified RGB-only framework free from temporal and odo-metric priors. ULVN systematically mitigates error accumulation by in-tegrating mapping, localization, and planning. Specifically, it constructsa robust 2D topological map directly from unstructured images via cali-brated geometric verification and maximum spanning forest refinement.For closed-loop execution, ULVN abandons sequential heuristics, utiliz-ing a graph-based belief propagation filter with entropy-adaptive fusionfor global localization and dynamic subgoal planning. Extensive experi-ments in simulation and real-world deployments demonstrate that ULVNsignificantly outperforms state-of-the-art methods.

ECCV 2026

UECP: Uncertainty-Enhanced Collaborative Perception

Kang Yang, Tianci Bu, Peng Wang, Deying Li, Jie Wen, Yongcai Wang

Collaborative perception serves as a pivotal solution to en-hance the perception capability of individual agents in autonomous driv-ing, where a core challenge lies in seeking reliable evidence to quantifyand weight the contribution of each participating agent. Existing meth-ods typically rely on a confidence map (co-trained with the detectionhead), which is, however, inherently correlated with the detection re-sults and thus fails to provide unbiased physical evidence. Furthermore,how to deeply integrate evidence into the cooperative fusion process re-mains an open question. To address these issues, this paper first pro-poses uncertainty map, a physically grounded and unambiguous met-ric for evaluating perception quality. This map is directly supervisedby real-time sensor signals (i.e., LiDAR point density), ensuring de-coupling from detection noise and thereby providing physical scenario-aware evidence for weighting agent contribution. Based on this map,we develop the Uncertainty-Enhanced Collaborative Perception (UECP)framework, centered on the Uncertainty-Aware Pyramid Fusion (UAPF)module. UAPF uses a coarse-to-fine strategy, with two key components:Uncertainty-Weighted Downsampling (UWD) for high-fidelity featurepreservation, and Uncertainty-Guided Residual Fusion (UGRF) to rein-force ego features, suppressing noise and ensuring robust fusion. Exten-sive experiments on real-world datasets show UECP outperforms SOTAmethods in effectiveness and robustness by embedding the uncertaintymap into fusion. Code will be publicly available.

ECCV 2026

Hi-Nav: Hierarchical Framework for Continuous Vision-Language Navigation via Map Guidance and Waypoint Reasoning

Zhiyu Zhou, Bin Guan, Wenbin Yang, Zhi Gao, Hao Fang

Despite remarkable advancements in Vision-and-LanguageNavigation (VLN), a significant sim-to-real gap remains. Most meth-ods are trained and evaluated in simplified or physics-limited simulators,which ignore the feedback loop between decision making and physical ex-ecution, resulting in unstable navigation performance. To bridge this gap,we propose Hi-Nav, a top-down hierarchical navigation framework thatdecomposes VLN into three controllable levels. The high-level moduleleverages a large language model to split long-horizon instructions intosub-tasks. At the middle level, an Interest Score Occupancy Map (ISOM)integrates instruction-relevant semantics with geometric traversability toperceive the surrounding environment, after which waypoint reasoning isperformed for route refinement. ISOM construction and waypoint reason-ing are jointly modeled within a unified multi-task learning framework,where a Q-Former fuses RGB-D observations and navigation instructionsinto shared query representations for efficient multi-task learning, aug-mented with learnable temporal embeddings to mitigate long-horizon for-getting. Finally, low-level behaviors are executed through ROS, enablingobstacle avoidance. We introduce a novel waypoint reasoning datasetfor comprehensive training and build a ROS/Gazebo VLN benchmarkto evaluate methods in a realistic simulation setting, which significantlyreduces the difficulty of sim-to-real transfer. Extensive experiments con-ducted in both Gazebo and the real world demonstrate strong perfor-mance, achieving up to 80% Success Rate (SR) with 0.65 m NavigationError (NE). Our code will be released at https://hiway-page.github.io/.

ECCV 2026

Uncertainty-aware tree height change regression

Max Gaber, Dimitri Gominski*, Jaime Revenga, Stefan Oehmcke, Rasmus Fensholt, Martin Brandt

Monitoring canopy height change is essential for understand-ing carbon sinks and forest dynamics. Remote sensing enables consis-tent, large-scale observations of such changes, increasingly integratedwith deep learning architectures such as Geospatial Foundation Models(GFMs). However, existing methods and datasets frame the problem asbinary change detection, which overlooks both the continuous nature ofchange, especially for vegetation, and the inherent uncertainty in labels.We present the Canopy Height Change (CHC) dataset, providing 3 mresolution continuous canopy height differences and associated spatiallyresolved uncertainties across 10 598 km2 of northern and western Spain.The dataset is paired with a co-located time series of PlanetScope satel-lite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuningGFMs. Furthermore, we evaluate state-of-the-art GFMs and highlightpromising directions and remaining challenges for advancing continuouscanopy height change estimation.

ECCV 2026

Two-Way Street: Efficient VSLAM using Collaborative In-Sensor and Off-Sensor processing

Hongyi Zhang, Laurie Bose, Piotr Dudek, Walterio Mayol-Cuevas

We present a novel implementation of Visual SimultaneousLocalisation and Mapping (VSLAM), whose computation is performedcollaboratively between a sensor-based front-end utilizing efficient in-pixel compute, and a PC-based back-end that sends pose estimationback to the sensor to improve feature matching. The front-end performsthe tasks of point-feature detection and tracking at up to 2000 Hz, whilethe PC back-end conducts the less immediate, higher level tasks, of map-ping and localisation at ≈ 200 Hz. This division of tasks is somewhatreminiscent of biological vision, in which the retina performs low levelcomputations, while the brain handles slower integrative functions. Onlysparse data is exchanged between these two ends, such as feature loca-tions, descriptor data, and sensor pose estimations. Essentially, each sideonly sends data which the other side requires to perform its respectivetask. This is a radical departure from the traditional visual pipeline,where dense image frames are streamed from sensor to PC. By compar-ison, our collaborative compute pipeline is extremely efficient, demon-strating a 10× improvement to speed and latency, > 1000× bandwidthand ∼ 20× reduction in power consumption per processed frame.

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