ECCV 2026 论文列表

Single-photon LiDAR (SPL) based on single-photon avalanche diode (SPAD) sensing enables time-resolved photon measurements with extreme sensitivity, offering unique potential for active 3D perception. However, real-world single photon perception remains fundamentally challenging due to unique measurement noise and complex multi-return transient phenomena, which jointly complicate geometric reconstruction and semantic scene understanding. Despite growing interest in SPAD-based sensing, existing studies are largely limited to simulated data or smallscale controlled captures. As a result, systematic evaluation of real-world single photon perception across depth estimation, multi-view reconstruction, and 3D semantic understanding remains underexplored. To bridge this gap, we introduce SP-TransientBench (STB), a real-captured multitask benchmark for single photon perception. STB provides 256 × 192 transient data for three tasks: 10 depth estimation samples, 9 reconstruction scenes, and 27 semantic sequences with 10,297 samples. Each view provides full time-of-flight histograms with multi-return behavior, standardized metadata, and calibrated camera poses for multi-view evaluation. We further provide 13-class 3D semantic annotations and histogram-domain multi-return annotations for selected scenes, enabling the study of raw transient cues. By providing dedicated data splits and evaluation protocols for each task, STB enables consistent and reproducible benchmarking of real-world single photon perception across multiple 3D vision problems. The dataset and code are available at: https: //huggingface.co/datasets/shuinb/SP-TransientBench

Visual Place Recognition (VPR) often fails under extremeenvironmental changes and perceptual aliasing. Beyond these limita-tions, standard systems cannot perform ’blind’ localization from verbaldescriptions alone, a capability critical for applications such as emer-gency response. To address these challenges, we introduce LaVPR, alarge-scale benchmark that extends existing VPR datasets with over650,000 rich natural-language descriptions. Using LaVPR, we investigatetwo paradigms: Multi-Modal Fusion for enhanced robustness and Cross-Modal Retrieval for language-based localization. Our results show thatlanguage descriptions yield consistent gains in visually degraded condi-tions, with the most significant impact on smaller backbones. Notably,adding language allows compact models to rival the performance of muchlarger vision-only architectures. For cross-modal retrieval, we establish abaseline using Low-Rank Adaptation (LoRA) and Multi-Similarity loss,which substantially outperforms standard contrastive methods acrossvision-language models. Ultimately, LaVPR enables a new class of lo-calization systems that are both resilient to real-world stochasticity andpractical for resource-constrained deployment. Our dataset and code areavailable at https://github.com/oferidan1/LaVPR.

The success of modern text-to-image generation is largely at-tributed to massive, high-quality datasets. Currently, these datasets arecurated through a filter-first paradigm that aggressively discards low-quality raw data based on the assumption that it is detrimental to modelperformance. Is the discarded bad data truly useless, or does it hold un-tapped potential? In this work, we critically re-examine this question. Wepropose LACON (Labeling-and-Conditioning), a novel training frame-work that exploits the underlying uncurated data distribution. Insteadof filtering, LACON re-purposes quality signals, such as aesthetic scoresand watermark probabilities as explicit, quantitative condition labels.The generative model is then trained to learn the full spectrum of dataquality, from bad to good. By learning the explicit boundary betweenhigh- and low-quality content, LACON achieves superior generation qual-ity compared to baselines trained only on filtered data using the samecompute budget, proving the significant value of uncurated data. Ourproject page is available at https://zhiyangliang.github.io/LACON.

Incremental scene reconstruction is essential for real-worldapplications. Although 3D Gaussian Splatting shows strong potential,most existing approaches require offline conversion of the optimized Gaus-sians into an intermediate implicit field for explicit mesh extraction,which hinders seamless integration with downstream tasks. To addressthis limitation, we propose a novel online framework that incrementallyreconstructs and updates high-fidelity explicit meshes by directly trian-gulating a dense geometric Gaussian representation, which supports bothhigh-quality rendering and incremental surface reconstruction. More-over, we present a direct meshing algorithm that efficiently extractsand updates the mesh from the Gaussian set. To ensure mesh accu-racy, we enforce a plane-based pulling constraint that dynamically aligns3D Gaussian primitives to the approximated local surface. Furthermore,our framework significantly reduces memory and computational overheadduring long-sequence processing by dynamically freezing fully optimizedhistorical regions. Experiments on public datasets demonstrate that ourmethod outperforms conventional Gaussian-based methods on both ren-dering quality and reconstruction accuracy.

Quantifying plant growth dynamics from sparse longitudinal3D observations is fundamental for agriculture and plant sciences. Yet,plants pose unique challenges: they undergo intricate non-rigid deforma-tions, exhibit changing topology as new organs emerge, and often lackexplicit temporal correspondences between consecutive data acquisitionsdue to newly formed tissue. Methods designed for general scenes struggleto model topology changes and asynchronous organ growth characteris-tic of plants. To address these challenges, we introduce GrowFields, acompositional dynamic neural field representation for organ-aware 4Dplant growth modelling from point cloud time series. Our approach de-composes a plant into its constituent organs and aligns each organ intoits own canonical coordinate frame, isolating intrinsic growth patternsfrom global plant motion. We then learn a shared continuous neural de-formation field that models temporal dynamics across all organs, condi-tioned on learnable per-organ latent codes capturing organ identity andgrowth characteristics. The resulting modular yet unified representationnaturally accommodates the asynchronous development of plant organswhile remaining grounded in the practical setting of organ-level planttracking. We evaluate GrowFields on growth sequences from four plantspecies, assessing geometric fitting and organ tracking accuracy usingmanually annotated leaf-tip trajectories. Results demonstrate consistentimprovements in spatial precision, temporal coherence, and morphologi-cal fidelity over a range of existing representations.

With the increasing development of Vision-Language Mod-els, it becomes imperative that their predictions are readily explainableto relevant stakeholders. However, the field of explainability has notkept pace with the multimodal surge. While recent Multimodal Explain-able AI (MxAI) methods generate explanations to attribute the interac-tion between different modalities, current evaluation protocols lack theground truth required to distinguish between true cross-modal reason-ing (e.g. spatial composition) and shallow cross-modal shortcuts (e.g.Bag-of-Words attribute matching). It remains unknown whether MxAImethods faithfully capture synergistic interactions or merely hallucinatereasoning on models acting as simple feature detectors. In this paper,we introduce GridVQA-X, the first diagnostic framework specificallydesigned to evaluate cross-modal explainability. Unlike natural datasets,GridVQA-X leverages a closed-world synthesis logic to generate unique,mathematically guaranteed explanations. We utilize this controlled envi-ronment to train paired ground-truth models on identical architectures:Mpure , which learns robust spatial-relational reasoning and Mspur , whichis structurally forced to rely on cross-modal shortcuts. This behavioraldivergence creates a rigorous testbed: a faithful explainer must reportdistinct reasoning pathways for each model.Our findings reveal that widely used methods fail to distinguish betweenmodels relying on genuine spatial-relational reasoning and those exploit-ing cross-modal shortcuts, highlighting a critical gap in capturing truecross-modal synergy and misrepresenting how multimodal models actu-ally make decisions. Our code is available at GitHub, and the dataset andmodels are available on Hugging Face at gridvqa-dataset and gridvqa-models, respectively.

Visible-infrared person re-identification aims to match pedes-trian images of the same identity captured by visible and infrared cam-eras, and is hindered by severe domain gaps due to the absence of colorin the infrared modality. Some methods employ text to supplement colorcues in infrared modality. However, they rely on global textual guidance,which hinders fine-grained alignment between textual color informationand local infrared semantics. To this end, this paper proposes a GranularSemantic Cognition (GSC) method, which leverages cross-modal sharedsemantics to facilitate the transfer of color features. Specifically, a Hierar-chical Refinement Module (HRM) progressively refines shared semanticprototypes via multi-stage interactions with pixel-wise features, yieldingfine-grained semantics. Based on these refined semantics, a Semantics-guided Adjustment Module (SAM) generates semantic-conditioned fu-sion weights to selectively aggregate textual color information in se-mantically relevant regions. In addition, a fusion-weighted identity con-trastive loss is proposed to alleviate semantic ambiguity across identi-ties by regularizing the semantics-guided fusion process. Extensive ex-perimental results conducted on the SYSU-MM01, RegDB and LLCMdatasets demonstrate the favorable performance.

4D occupancy forecasting models the spatio-temporal evo-lution of 3D scenes and is crucial for autonomous driving, especially forcorner-case simulation. Existing methods often rely on discrete tokeniza-tion followed by autoregressive prediction, yet struggle with geometricdistortion in static structures and inconsistent temporal coherence overthe forecasting horizon. In this work, we propose a Geometry-AwareSpatio-Temporal context modeling method (GAST) for 4D occupancyforecasting, built upon progressive explicit-implicit generation and dual-path spatio-temporal modeling. Specifically, the generation module pro-duces per-frame occupancy with high geometric fidelity and semanticplausibility through pose-driven warping, motion-aware feature modula-tion, and attention-based feature refinement. Subsequently, the spatio-temporal module enhances spatial consistency through global contextaggregation while capturing scene evolution through temporal dynam-ics extraction. This unified design enables joint optimization of histori-cal reconstruction and future forecasting in an end-to-end manner. Ex-tensive experiments on Occ3D-nuScenes demonstrate the superiority ofour method, outperforming the state-of-the-art by 7.67% in mIoU and6.44% in IoU with a 2.84× speedup, while maintaining strong perfor-mance in long-term forecasting. Our source code is publicly available athttps://github.com/chenst27/GAST.

Generative models like Di_x001B_usion Models and Flow Matchinghave demonstrated remarkable capabilities in synthesizing high-_x001C_delitydriving videos, but are severely constrained by high inference latencydue to the requirement of extensive sampling steps. We argue that thisine_x001E_ciency stems from the prevailing reliance on a standard Gaussiansource distribution, where consecutive frames are initialized as indepen-dent Gaussian noise. This paradigm disregards the rich spatiotemporalcorrelations inherent in driving videos, compelling the model to regener-ate deterministic scene structures existing in previous frames from noise,which is both computationally redundant and prone to geometric incon-sistency. To address this problem, we propose GeoFlow, a novel frame-work designed to achieve e_x001E_cient driving video generation by harnessingexplicit geometric priors. Instead of sampling from standard Gaussiannoise, we leverage multi-view geometry and spatially-adaptive noise in-jection to construct a Geometry-Aligned Prior (GAP) distribution asstarting point. This initialization bridges the gap between source dis-tribution and data distribution, yielding a signi_x001C_cantly straighter andshorter sampling trajectory. Extensive experiments demonstrate that Ge-oFlow can achieve remarkable e_x001E_ciency of both training and inference:merely several hours of _x001C_ne-tuning on baseline models can signi_x001C_cantlyboost few-step generation quality, while fully converged training drasti-cally reduces number of inference steps required for state-of-the-art videogeneration.

Lane topology reasoning aims to construct a lane graph fromonboard sensor observations. Existing methods follow a detection and as-sociation paradigm that treats each lane instance independently, leadingto geometric inconsistency at connected endpoints and incomplete graphsdue to visual occlusions. To address these issues, we propose TopoGPT,a generative framework that learns the geometry prior from typical lanegraph structures through autoregressive sequence modeling. Specifically,we construct a large-scale map dataset comprising 3.3M scenes. For eachlane graph, a lane tokenizer serializes it into discrete tokens, while a scenecontext encoder converts it into a rasterized image and extracts globalfeatures as scene tokens. We pre-train an autoregressive lane sequencetransformer via scene-conditioned next-token prediction, endowing themodel with the geometry prior over lane graph structures. Building uponthis prior, a perception adapter aligns BEV features from multi-view im-ages with the pre-trained scene condition, transferring the learned geom-etry prior to sensor-based lane graph prediction. On the OpenLane-V2benchmark, TopoGPT outperforms existing methods by an average of+6.4 on lane-level and +11.6 on point-level metrics, and produces geo-metrically consistent and structurally complete lane graphs. Our projectpage is available at https://buaa-colalab.github.io/topogpt_page.

We introduce a novel learning problem: decoding gaze intonatural language descriptions of human goals across diverse visual tasks.Unlike prior work, which frames gaze decoding as a discriminative taskover predefined categories, we formulate it as a generative learning prob-lem: training a model to produce free-form descriptions that capture therich nuances and open-ended nature of human intentions beyond fixedlabels. To this end, we introduce Gazette, the first gaze-to-text decod-ing framework. Based on multimodal large language models (MLLMs),Gazette learns to decode gaze scanpaths into natural language for goalsthat may extend beyond categorical labels and require articulation innatural language. To help Gazette filter out individual differences in gazebehavior and learn the goal-specific spatiotemporal dynamics crucial forgenerating accurate natural language goal descriptions, we propose anovel strategy that leverages the encyclopedic knowledge and reason-ing abilities of a large language model to synthesize natural languageexplanations of goal-directed attentional behavior called think-aloud tran-scripts. Instruction tuning on these synthetic narratives allows Gazetteto achieve state-of-the-art performance in gaze decoding across mul-tiple tasks, demonstrating its generalizability and versatility, therebyenabling gaze to serve as a powerful, non-intrusive cue for inferringhuman goals and intentions in diverse scenarios. Code is available athttps://github.com/cvlab-stonybrook/Gazette.

Curvilinear object segmentation, including vessels and cracks, is challenging due to extreme spatial sparsity and topological fragility, where small local errors can cause severe structural disconnections. Meanwhile, modern segmentation pipelines increasingly rely on strong but hard-to-modify foundation encoders whose heavy downsampling limits fine structural recovery. Motivated by this, we focus on the post-encoder stage and study two recurring and actionable failure modes: a reconstruction bottleneck in high-resolution feature restoration and a decision bottleneck in binarization. We present PEPA, a lightweight Post-Encoder Plug-in Adapter for 2D curvilinear segmentation pipelines with accessible decoder/head features and target, query, or class descriptors. PEPA couples (i) Target-Conditioned Snake Upsampling (TCSU), which uses target-conditioned continuous snake-like sampling to better recover thin and tortuous structures during upsampling, and (ii) Target-Adaptive Differentiable Thresholding (TADT), which predicts target-specific thresholds and optimizes a soft-threshold surrogate with explicit safeguards against trivial bias shifting. Under this post-encoder interface, PEPA can be attached to both prompt-based decoders and conventional dense predictors. Experiments on five medical and industrial benchmarks show that adding PEPA to frozen-encoder baselines yields consistent improvements, with gains in topological connectivity (clDice) typically exceeding those in region overlap (IoU), indicating improved structural continuity. With only ∼0.26M additional parameters, PEPA offers a practical postencoder enhancement for structure-centric segmentation.

Generative models for volumetric medical images have foundmany applications in medical imaging, ranging from data augmentationto serving as priors for inverse problems. For these applications, generat-ing high-resolution 3D images with strong controllability is essential butremains highly challenging. Existing approaches typically control gener-ation either through radiology reports used as text prompts or throughfull image segmentation. While text-based prompting is flexible, it pro-vides limited spatial control over the location, shape, and boundary ofabnormalities. In contrast, segmentation-based methods receive precisespatial guidance but are restrictive in requiring full-organ annotations.In this work, we propose MedSynV2, a flexible multimodal framework forcontrollable volumetric image generation that supports input from radi-ology reports and segmentation prompts (both optional). Our approachallows users to provide segmentation of a specific anatomy or abnormalitywithout requiring full-organ annotations. The semantic meaning of thesegmentation mask is specified through an accompanying text descrip-tion, resulting in a highly flexible and scalable conditioning mechanism.We develop a memory-e!cient architecture based on a modified di"usiontransformer that jointly processes image and segmentation tokens. Themodel further incorporates gated attention to e"ectively attend to longradiology reports. Experiments demonstrate that our method achievesstate-of-the-art perceptual and semantic scores (e.g., → 24% relative im-provement in mean FID), generates high-resolution anatomically consis-tent CT volumes, and improves data e!ciency when used for data aug-mentation. Radiologists’ evaluation further confirms strong alignmentbetween generated and real medical images. We also explore the abilityof the model to generalize towards concepts beyond training data. Codewill be released at https: // github. com/ batmanlab/ MedSynV2 .

The structural vulnerabilities of point cloud–based 3D objectdetectors remain poorly understood. Prior work has studied adversarialrobustness primarily on isolated 3D object models, while recent LiDARspoofing attacks target richer and more realistic driving scenes but focusmainly on physical realizability rather than understanding detector be-havior or attack efficiency. In this work, we investigate how LiDAR-baseddetectors rely on spatial evidence in complex scenes and whether these re-liance patterns can be exploited to induce failures more efficiently. To thisend, we propose an explainability-guided adversarial analysis methodology.We introduce the Saliency-LiDAR (SALL) method, which aggregates In-tegrated Gradient attributions across scenes to produce universal saliencymaps for LiDAR-based 3D object detectors. Guided by these maps, wedesign the Explainability-aware Frustum Attack (EFA), which selectivelyperturbs only the most influential frustums rather than uniformly attack-ing entire object regions. Experiments on KITTI and nuScenes, acrossdetectors such as PointPillars and SECOND, show that EFA reducesdetection recall by more than 15 percentage points while requiring 25–50%fewer perturbed frustums than the SOTA non–saliency-aware baseline.These findings reveal that modern 3D detectors concentrate discrimina-tive evidence in a small subset of spatial regions, exposing a structuralrobustness vulnerability in current LiDAR perception systems. Our codeis released at https://github.com/SecMindLab/Saliency_LiDAR.

Developing effective dense crowd simulation is challengingdue to the scale gap between macroscopic collective coherence and mi-croscopic individual realism. Existing macroscopic (Eulerian) methodscapture global motion patterns but lack individual trajectories, whereasmicroscopic (Lagrangian) methods model individual behaviors yet oftenfail to preserve systemic consistency in dense scenarios. To address this,we propose the Eulerian–Lagrangian Hydrodynamics-Informed NeuralNetwork (ELHINN), a unified cross-scale framework that couples macro-scopic velocity evolution with microscopic trajectory refinement by usingevolved Eulerian velocity fields as physical priors to guide Lagrangian tra-jectories. For velocity evolution, we develop an enhanced Hydrodynamics-Informed Neural Network (HINN++) that incorporates a learnable gov-erning equation with Kolmogorov–Arnold Network (KAN) residual cor-rection and environmental boundary conditions, enabling accurate mod-eling of complex nonlinear dynamics under varying scenarios. For tra-jectory refinement, we use a Physics-Informed Neural Network (PINN)augmented with an entrance-aware resampling strategy (EARS) andcollision-avoidance constraints to ensure stability and fidelity. Experi-ments on two real-world crowd datasets demonstrate that ELHINN out-performs existing methods in simulating dense crowds across both scales.Codes are available at https://github.com/shanshan-zys/ELHINN.

End-to-End autonomous driving (E2E-AD) systems face chal-lenges in lifelong learning, including catastrophic forgetting, difficulty inknowledge transfer across diverse scenarios, and spurious correlationsbetween unobservable confounders and true driving intents. To addressthese issues, we propose DeLL, a Deconfounded Lifelong Learning frame-work that integrates a Dirichlet process mixture model (DPMM) withthe front-door adjustment mechanism from causal inference. The DPMMis employed to construct two dynamic knowledge spaces: a trajectoryknowledge space for clustering explicit driving behaviors and an implicitfeature knowledge space for discovering latent driving abilities. Leverag-ing the non-parametric Bayesian nature of DPMM, our framework en-ables adaptive expansion and incremental updating of knowledge withoutpredefining the number of clusters, thereby mitigating catastrophic for-getting. Meanwhile, the front-door adjustment mechanism utilizes theDPMM-derived knowledge as mediators to deconfound spurious correla-tions, such as those induced by sensor noise or environmental changes,and enhances the causal expressiveness of the learned representations.Additionally, we introduce an evolutionary trajectory decoder that en-ables non-autoregressive planning. To evaluate the lifelong learning per-formance of E2E-AD, we propose new evaluation protocols and metricsbased on Bench2Drive. Extensive evaluations in the closed-loop CARLAsimulator demonstrate that our framework significantly improves adapt-ability to new driving scenarios and overall driving performance, whileeffectively retaining previously acquired knowledge.

Vision-language-action models have advanced robotic ma-nipulation but remain constrained by reliance on the large, teleoperation-collected datasets dominated by the static, tabletop scenes. We propose asimulation-first framework to verify VLA architectures before real-worlddeployment and introduce MobileManiBench, a large-scale benchmarkfor mobile-based robotic manipulation. Built on NVIDIA Isaac Sim andpowered by reinforcement learning, our pipeline autonomously gener-ates diverse manipulation trajectories with rich annotations (languageinstructions, multi-view RGB–depth–segmentation images, synchronizedobject/robot states and actions). MobileManiBench features 2 mobileplatforms (parallel-gripper and dexterous-hand robots), 2 synchronizedcameras (head and right wrist), 630 objects in 20 categories, 5 skills(open, close, pull, push, pick) with over 100 tasks performed in 100 re-alistic scenes, yielding 300K trajectories. This design enables controlled,scalable studies of robot embodiments, sensing modalities, and policy ar-chitectures, accelerating research on data efficiency and generalization.We benchmark representative VLA models and report insights into per-ception, reasoning, and control in complex simulated environments, withall code, datasets, and models publicly released at our project website:https://dexhand.github.io/MobileManiBench/.

Visual Active Tracking (VAT) requires an embodied agent tocontrol cameras to follow a designated target, which is essential for ap-plications like robot navigation and security monitoring. However, trans-ferring VAT trackers to the real world faces two primary bottlenecks: (1)severe visual domain shifts between simulated environments and the realworld, leading to performance collapse during deployment; and (2) tar-get distribution shift caused by similar-looking distractors in real-worldscenes. To address these, we propose VATA, a novel test time adaptationmethod with two complementary strategies. First, we introduce a CriticValue Maximization strategy that leverages value signals from a criticmodel to filter reliable test samples, enabling unsupervised model up-dates to mitigate visual shift. Second, we propose an Action Smooth-ness Regularization term that exploits the continuity of real-worldtarget motion to bound action variations, correcting irrational actionsunder target shift. Experiments demonstrate VATA achieves a 24.4%SR improvement on the EVT-Benchmark and reaches 80% TSR in 10real-world scenarios (vs. 30% by the SOTA TTA method), providing arobust and plug-and-play solution for VAT deployment.

Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.