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ECCV 2026

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

Zhiyuan Zhou, Ruofeng Liu, Taichi Liu, Weijian Zuo, Shanshan Wang, Zhiqing Hong, Desheng Zhang

Accurate, dense depth estimation is crucial for robotic per-ception, but commodity sensors often yield sparse or incomplete measure-ments due to hardware limitations. Existing RGBD-fused depth comple-tion methods learn priors jointly conditioned on training RGB distri-bution and specific depth patterns, limiting domain generalization androbustness to various depth patterns. Recent efforts leverage monoculardepth estimation (MDE) models to introduce domain-general geomet-ric priors, but current two-stage integration strategies relying on explicitrelative-to-metric alignment incur additional computation and introducestructured distortions. To this end, we present Any2Full, a one-stage,domain-general, and pattern-agnostic framework that reformulates com-pletion as a scale-prompting adaptation of a pretrained MDE model.To address varying depth sparsity levels and irregular spatial distribu-tions, we design a Scale-Aware Prompt Encoder. It distills scale cuesfrom sparse inputs into unified scale prompts, guiding the MDE modeltoward globally scale-consistent predictions while preserving its geomet-ric priors. Extensive experiments demonstrate that Any2Full achievessuperior robustness and efficiency. It outperforms OMNI-DC by 32.2%in average AbsREL and delivers a 1.4× speedup over PriorDA with thesame MDE backbone, establishing a new paradigm for universal depthcompletion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.

ECCV 2026

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution

Zehao Wang, Yihan Zeng, Zidong Gong, Yuanfan Guo, Feng Zhu, Hongzhi Zhang, Wei Zhang, Wangmeng Zuo

Post-training via Supervised Fine-Tuning (SFT) and Rein-forcement Learning (RL) is crucial for enhancing reasoning in Multi-modal Large Language Models (MLLMs), yet existing paradigms of-ten reach a performance bottleneck due to the limitations of staticdata. While current methods leverage self-reflection or self-evolution topush these boundaries, they still suffer from cognitive drift and hallu-cinated reasoning paths caused by low-quality synthetic data. To ad-dress these challenges, we propose Anchor E volution (AnE), a newparadigm that integrates truth-anchored data curation and model evo-lution, achieving faithful and steady performance gains at the reason-ing frontier. Specifically, we propose Truth Anchor Expansion, whichpinpoints the model failing frontier via trajectory rollouts and lever-ages ground-truth databases to retrieve high-fidelity anchors for faithfuldata curation. Subsequently, we introduce the Scaffold-Stripping Mech-anism to internalize reasoning capabilities. This mechanism first an-chors reasoning paths via scaffold-augmented supervision to mitigatethe learning complexity and distribution drift of direct SFT on rawdata, then leverages RL to strip the scaffold template, thereby effec-tively transitioning the reasoning paths into intrinsic model capabilities.Experimental results on multimodal reasoning benchmarks show thatour method substantially advances the model performance frontier, im-proving the base model by 10.3% across eight multimodal benchmarksand achieving state-of-the-art results. The code will be publicly availableat https://github.com/wangzehao0704/AnE.

ECCV 2026

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

Hodaya Krakover, Meir Yossef Levi, Eyal Gofer, Guy Gilboa

Adversarial attacks pose a challenge to the reliability of deeplearning models, motivating effective detection methods. Existing tech-niques often rely on attack-specific assumptions, access to adversarialsamples, or knowledge of the underlying classifier (white-box). We pro-pose A4 D (Attack- and Architecture-Agnostic Adversarial Detector), acompletely black-box, zero-shot adversarial attack detection frameworkthat utilizes prompt-based similarity scores derived from CLIP. To thebest of our knowledge this is the first attempt to utilize CLIP for sucha task. The method is based on two key observations: (i) CLIP is sen-sitive even to small imperceptible non-semantic perturbations; (ii) Theshift in CLIP embedding space is not arbitrary and can be used as arobust attack indicator. Experiments across multiple attacks, datasetsand classifiers validate that A4 D achieves SOTA detection results in theattack-agnostic and classifier-agnostic setting.

ECCV 2026

XPos3R: Cross-Modal Transformer for Intraoperative 2D/3D Registration

Shiyan Su, Ruyi Zha, Hongdong Li, Xuelian Cheng, Zongyuan Ge

Intraoperative 2D/3D registration, which aligns live X-rayimages with preoperative volumes, is essential for image-guided interven-tions. Previous regression-based methods suffer from limited generaliza-tion, thus requiring time-consuming patient-specific retraining. Inspiredby recent geometry foundation models such as DUSt3R, we proposeXPos3R, a generalizable pose regression method that eliminates preop-erative preparation. Unlike existing geometry models designed for homo-geneous inputs, XPos3R extends this paradigm to multi-modal inputs,namely 2D X-rays and 3D volumes. Specifically, we introduce an asym-metric encoder–decoder architecture that improves cross-modal featurealignment while maintaining computational efficiency. To scale trainingunder limited medical data, we adopt an anatomy-specific data cura-tion strategy and construct million-scale synthetic datasets. Evaluatedon real-world benchmarks, a single pretrained XPos3R surpasses patient-specific methods in both accuracy and robustness. With test-time opti-mization completed in seconds, it further reduces the 3D error to <4 mmand the reprojection error to <1 mm. The strong generalization, accu-racy, and efficiency of XPos3R highlight its clinical potential, while itsasymmetric framework may inspire broader cross-modal vision geometrytasks.

ECCV 2026

WildProp: Visual Estimation of Wildlife Body Proportions at Scale

Mustafa Chasmai, Aaron Sun, Subhransu Maji

Population-level morphometric measurements are central toecological and evolutionary studies, but traditionally require controlledimaging or physical specimen handling, limiting their scalability. Wepresent WildProp, a framework for estimating wildlife body-proportiondistributions directly from large-scale, unconstrained image repositories.We cast morphometric estimation as a retrieval-driven correspondenceproblem: given a single user-annotated canonical image, WildProp per-forms pose-aware retrieval using foundation model features, transferspart endpoints via dense patch-level matching, filters predictions us-ing geometric consistency, and aggregates measurements across retrievedimages to estimate length-ratio distributions. Unlike supervised keypointpipelines, our approach adapts to arbitrary species and user-defined bodyparts without per-species training. Evaluations on three large morpho-metric datasets spanning birds and amphibians show median relative er-rors of 10–20%. We further demonstrate broad applicability through casestudies measuring proportions across diverse taxa, including birds, frogs,insects, and flowers. Ablations show that pose-aware retrieval is criticalfor stable estimation, while robust aggregation mitigates keypoint andpose noise. Our results suggest that carefully filtered 2D correspondencesover web-scale imagery can provide scalable morphometric proxies forcomparative analyses across taxa, geography, and seasonality.

ECCV 2026

Wavelet-Driven Cross-Domain Consistency for Mixed-Supervised 3D Tumor Segmentation

Tianzhong Lan, Weili Jiang, Yisong Liu, Yi Zhou, Junqi Bai, Si Yong Yeo, Xulei Yang, Min Zhu

Accurate 3D tumor segmentation is vital for clinical decision-making, but pixel-wise annotation is expensive. Mixed-supervised learn-ing combines a small set of mask labels with abundant box labels, yetreconstruction-kernel heterogeneity creates large domain gaps. We pro-pose WCC4MS, a jointly optimized lesion-centered framework that mod-els kernel-related frequency shifts via a 3D discrete wavelet transform.The input volume is decomposed into low- and high-frequency com-ponents, and the scaled high-frequency coefficients generate syntheticsmooth and sharp variants. A dual-branch architecture learns from maskand box annotations with task-specific decoders, while a cross-branchKL-divergence constraint enforces semantic consistency. An auxiliarydomain-aware head encourages quality-sensitive features and improvesrobustness to domain shift. At inference, only the segmentation branch isused on candidate-centered patches; the box branch is used only duringtraining to exploit weak annotations. Experiments across three mask-annotated CT datasets and one box-only dataset show consistent gainsin segmentation accuracy and cross-domain generalization compared toprior mixed- and weakly supervised methods.

ECCV 2026

Wan-R1: Verifiable-Reinforcement Learning for Generalizable Video Reasoning

Ming Liu, Yunbei Zhang, Shilong Liu, Liwen Wang, Wensheng Zhang

Video generation models produce visually coherent content but struggle with tasks requiring spatial reasoning and multi-step planning. Reinforcement learning (RL) offers a path to improve generalization, but its effectiveness in video reasoning hinges on reward design—a challenge that has received little systematic study.WeinvestigatethisproblembyadaptingGroupRelativePolicyOptimization (GRPO)toflow-basedvideomodelsandtrainingthemonmaze-solvingandrobotic navigationtasks.Wefirstshowthatmultimodalrewardmodelsfailcatastrophically in this setting. To address this, we design verifiable reward functions grounded in objective task metrics. For structured game environments, we introduce a multicomponent trajectory reward that is verifiable against ground-truth optimal paths. For robotic navigation, where ground truth is given only as a reference rollout, we propose an embedding-level reference-anchored reward. Our experiments show that RL fine-tuning with verifiable rewards improves generalization. For example, on complex 3D mazes, our model improves exact match accuracy by 29.1% over the SFT baseline, and on trap-avoidance tasks by 51.4%. Our systematic reward analysis reveals that verifiable rewards are critical for stable training, while multimodal reward models could lead to degenerate solutions. These findings establish verifiable reward design as a key enabler for robust video reasoning.

ECCV 2026

Urban Boundaries, Social Barriers: A Benchmark and Vision-Centric Framework for Mapping Gated Communities and Equity Implications

Minwei Zhao, Weiming Zhang, Jiawang DU, Qiming Liu, Weiming Zhuang, Pei Nie, Cai Wu

Communities are fundamental spatial units that shape urbanform and social life. Whether a residential compound is spatially openor enclosed affects mobility, access to public services, and equity, yetstudies of Chinese fengbi xiaoqu remain largely qualitative or small-scale,limiting reproducible city-scale analysis. We address this gap by introduc-ing GBA-GCs, a metropolitan-scale multimodal benchmark for locallygrounded gated/open community recognition in China’s Greater BayArea, covering 37,444 residential compounds with aligned boundary poly-gons, high-resolution satellite imagery, Chinese metadata, and structuredattributes, together with expert-verified labels, inter-annotator reliability,and official evaluation splits. Built on this benchmark, we present Multi-modal Classifier for Gated Community (MCGC), a vision-centricmultimodal framework based on DINOv3-SAT that fuses imagery, text,and structured cues via modality-aware cross-attention and adaptivegating to mitigate modality imbalance. MCGC consistently outperformsstrong unimodal and multimodal baselines. Finally, we apply the validatedmodel to metropolitan-scale mapping and report equity-oriented findingsincluding spatial clustering of GCs, privatized green space, and reducedpedestrian connectivity. The benchmark, code, and release documentationare available at https://github.com/MinweiZhao/GBA-GCs.

ECCV 2026

Unpaired Geometry-Guided Sim2Real Translation for Autonomous Driving

Xinzhuo Chen, Shijie Wang, Chao Gao, Jinguang Gu, Gongjin Lan

Simulation is indispensable for autonomous driving, yet thepronounced visual Sim2Real gap severely limits the cross-domain gen-eralization of perception models. To bridge this gap, we propose Cross-Domain Control Transfer (CDCT), a diffusion-based Sim2Real frame-work for translating synthetic images into realistic counterparts withoutpaired supervision. CDCT features a novel cross-domain score composi-tion mechanism that injects domain-agnostic geometry guidance, derivedfrom synthetic-domain score differences, into a real-world appearanceprior. This ensures strict geometric and semantic consistency withoutpaired real-world spatial conditions. However, naively incorporating mul-tiple geometric conditions often incurs a prohibitive parameter overhead.To address this, our Lightweight Multi-Condition Adapter (LMCA) mod-ule processes diverse rendering buffers simultaneously, eliminating thecomputational redundancy of standard multi-branch architectures. Ex-tensive experiments on the CARLA-to-Cityscapes benchmark demon-strate that CDCT yields substantial zero-shot performance gains ondownstream perception tasks. Crucially, joint training with our generateddata surpasses the performance of real-world-only training, showcasingits immense potential for autonomous driving.

ECCV 2026

UEval: A Benchmark for Unified Multimodal Generation

Bo Li, Yida Yin, Wenhao Chai, Xingyu Fu, Zhuang Liu

We introduce UEval, a benchmark to evaluate unified mod-els, i.e., models capable of generating both images and text. UEval com-prises 1,000 expert-curated questions that require both images and textin the model output, sourced from 8 real-world tasks. Our curated ques-tions cover a wide range of reasoning types, from step-by-step guides totextbook explanations. Evaluating open-ended multimodal generation isnon-trivial, as simple LLM-as-a-judge methods can miss the subtleties.Unlike prior methods relying on multimodal Large Language Models(MLLMs) to rate image quality or text accuracy, we design a rubric-basedscoring system in UEval. For each question, reference images and text an-swers are provided to an MLLM to generate an initial rubric, consisting ofmultiple evaluation criteria, and human experts then refine and validatethese rubrics. In total, UEval contains 10,417 validated rubric criteria,enabling fine-grained automatic scoring. UEval is challenging for currentunified models: GPT-5-Thinking scores only 66.4 out of 100, while thebest open-source model reaches merely 49.1. We observe that reasoningmodels often outperform non-reasoning ones, and transferring reasoningtraces from a reasoning model to a non-reasoning model narrows thegap. This suggests that reasoning may be important for tasks requiringcomplex multimodal understanding and generation. UEval is availableat https://huggingface.co/datasets/zlab-princeton/UEval

ECCV 2026

TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields

Haoxuan Li, Ziya Erkoç, Daniele Sirigatti, Vladislav Rosov, Lei Li, Angela Dai, Matthias Niessner

We present TriFlow, a new generative approach for produc-ing compact 3D meshes with artist-like triangle topology directly frominput geometry conditions such as signed distance fields. Our key insightis to represent mesh topology as a nearest-vertex vector field (NVF) de-fined over the surface, where each point encodes its association to thenearest triangle vertex in the local barycentric frame. We train a latentflow-matching model to synthesize this field, enabling topology genera-tion conditioned on the input geometry. To extract a coherent mesh, wecluster surface regions using the generated NVF and guide a constrainedquadric error metric mesh simplification with topology-aware optimiza-tion. This yields output meshes that closely match the input geometrywhile exhibiting structured, artist-like connectivity. Experiments demon-strate that TriFlow achieves stronger generalization and significantlyimproved topology quality compared to state-of-the-art learning-basedapproaches, alongside 90% lower Chamfer Distance and an 8× speedup.

ECCV 2026

TRAM: Finetuning-Free Test-Time Adaptation for Generalized Face Anti-Spoofing with Only a Few Bonafide Samples

Wang QIRUI, Si-Qi Liu

Generalized face anti-spoofing (FAS) has attracted increas-ing attention due to the need for robustness in unseen scenarios. Do-main adaptation methods can improve performance by leveraging target-domain information. However, in FAS, collecting spoof samples is signif-icantly more expensive than acquiring a few bonafide samples, makingit impractical to gather diverse attack types and finetune models foreach deployment scenario. To address this challenge, we propose Text-guided RelAtionship Modeling (TRAM), a finetuning-free test-time do-main adaptation approach for generalized FAS using only a few bonafidesamples. Since samples within a target domain share common domainfactors, we treat genuine sample as the anchor and model its relation-ship with incoming inputs to mitigate domain bias. To learn discrimina-tive spoof cues from these relationships, we introduce a Relative Image-Pair-Text Contrastive Learning strategy that leverages fine-grained FASrelation prompts. Furthermore, to compensate for the lack of detailedspoof annotations, we design a Fine-grained Relation Prompt Learn-ing strategy that mines low-level photometric discrepancies through pre-computed measurements to generate robust prompts. Extensive exper-iments on eight FAS datasets with typical variations demonstrate thatTRAM achieves consistent improvements over state-of-the-art methodswhile using only a few bonafide samples from the target domain (as fewas five). Comprehensive ablation studies further validate the effectivenessof each component.

ECCV 2026

To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models

Siru Jiang, Yuwei Liang, Jian Liang, Ran He, Tieniu Tan

Test-time adaptation (TTA) has emerged as a prominentstrategy for adapting vision-language models to distribution shifts dur-ing inference. We conduct a per-sample analysis of model predictionsbefore and after adaptation, and observe two failure modes in existingTTA methods that echo previous work. Adaptations are frequently neg-ligible, yielding no change in the model’s predictions, and more severely,they can be detrimental by flipping previously correct predictions to in-correct ones. This naturally raises a question: Can we identify and skipsuch negligible or harmful adaptations? In this work, we introduce a newproblem of selective adaptation, which aims to determine whether agiven test sample should undergo adaptation or be skipped. To this end,we propose Cross-Augmentation Similarity (CAS), a simple baseline thatperforms adaptation only when predictions across augmented views ex-hibit low similarity. Notably, CAS not only preserves but in some casesimproves overall accuracy, even when skipping nearly 85% of the adap-tation process. We hope other researchers will explore this new directionand surpass the performance of our baseline. Our code is available athttps://github.com/sirujiang/selective-adaptation.

ECCV 2026

TiCRL: Textual Image Classification with Reinforcement Learning-Based Curriculum Learning

Gayoung Kim, Yuncheol Kang

Textual image classification remains challenging due to thediversity of document layouts and textual structures. We propose TiCRL(Textual Image Classification with Reinforcement Learning-based Cur-riculum Learning), which combines a text-independent difficulty mea-surer with an RL-based training scheduler. The measurer estimates sam-ple difficulty using visual-textual features and loss signals without re-quiring OCR, while the RL agent dynamically adapts the curriculumbased on the learner’s state. TiCRL substantially improves both accu-racy and data efficiency. On a lightweight CNN trained from scratch,it achieves 83.67% accuracy using only 82% of the training data, out-performing curriculum learning baselines by up to 13.8%p. Applied toa strong transformer baseline (DiT), TiCRL reaches 93.02% accuracyusing only 58% of the data, surpassing transformer baselines trained on80%. The learned curriculum policy generalizes to new datasets and ar-chitectures without retraining, demonstrating that RL-based curriculumlearning is an effective and efficient training strategy for textual imageclassification.

ECCV 2026

The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding

Weichen Fan, Haiwen Diao, Quan Wang, Dahua Lin, Ziwei Liu

Deep representations across modalities are inherently inter-twined. In this paper, we systematically analyze the spectral character-istics of various semantic and pixel encoders. Interestingly, our studyuncovers a highly inspiring and rarely explored correspondence betweenan encoder’s feature spectrum and its functional role: semantic encodersprimarily capture low-frequency components that encode abstract mean-ing, whereas pixel encoders additionally retain high-frequency informa-tion that conveys fine-grained detail. This heuristic finding offers a uni-fying perspective that ties encoder behavior to its underlying spectralstructure. We define it as the Prism Hypothesis, where each datamodality can be viewed as a projection of the natural world onto ashared feature spectrum, just like the prism. Building on this insight,we propose Unified Autoencoding (UAE), a model that harmonizessemantic structure and pixel details via an innovative frequency-bandmodulator, enabling their seamless coexistence. Extensive experimentsdemonstrate that UAE effectively unifies semantic abstraction and pixel-level fidelity within a single latent space, achieving state-of-the-art per-formance. Moreover, we show that UAE can be directly applied to pixel-space modeling, significantly improving both FID and IS over the vanillaJIT baseline. Code is avaliable at: https://github.com/WeichenFan/UAE.

ECCV 2026

The Path to Reconciling Quality and Safety Alignment in Text-to-Image Generation

Shouwei Ruan, Zhenyu Wu, Yao Huang, Ruochen Zhang, Yitong Sun, Caixin Kang, Shiji Zhao, Weijun Qin, Jingzhi Li, Xingxing Wei

006 Content safety is a fundamental challenge for text-to-image 006007 (T2I) models, yet prevailing methods enforce a debilitating trade-off 007008 between safety and generation quality. We argue that mitigating this 008009 trade-off hinges on addressing systemic challenges in current T2I safety 009010 alignment across data, methods, and evaluation protocols. To this end, 010011 we introduce a unified framework for synergistic safety alignment. First, 011012 to overcome the flawed data paradigm that provides biased optimiza- 012013 tion signals, we develop LibraAlign-100K, the first large-scale dataset 013014 with dual annotations for safety and quality. Second, to address the my- 014015 opic optimization of existing methods focus solely on safety reward, we 015016 propose Synergistic Preference Optimization (T2I-SPO), a novel align- 016017 ment algorithm that extends the DPO paradigm with a composite re- 017018 ward function that integrates generation safety and quality to holistically 018019 model user preferences. Finally, to overcome the limitations of quality- 019020 agnostic and binary evaluation in current protocols, we introduce the 020021 Unified Alignment Score, a holistic, fine-grained metric that fairly quan- 021022 tifies the balance between safety and generative capability. Extensive 022023 experiments demonstrate that T2I-SPO achieves state-of-the-art safety 023024 alignment against a wide range of NSFW concepts, while better main- 024025 taining the model’s generation quality and general capability. This 025026 paper contains harmful text and image examples. 026

ECCV 2026

Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception

Oleh Kolner, Thomas Ortner, Stanislaw Wozniak, Angeliki Pantazi

State-of-the-art vision models process images in their en-tirety, lacking the ability to selectively zoom in on relevant regions. Thislimitation is particularly acute in scenarios where processing must beconditioned on a specific task – such as instance detection, which re-quires localizing a specific object in a high-resolution, cluttered scene. Insuch settings, critical details are easily lost as images are often resizedto match the model dimensions and computational constraints. We in-troduce Coarse-to-Fine Glimpse-based Active Perception (CF-GAP), atask-driven front-end that enhances high-resolution processing of exist-ing instance detectors. CF-GAP selectively directs a sequence of limited-view glimpses across the scene, utilizing task information to iterativelyrefine focus on the most relevant regions. These localized regions arethen processed at high resolution by a downstream instance detector.By avoiding full-image processing and eliminating irrelevant confound-ing information, CF-GAP improves Average Precision (AP) by up to20% across various state-of-the-art instance detectors on the HR-InsDetand Robotools benchmarks, while further enabling lightweight detectorsto outperform their larger counterparts.

ECCV 2026

Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?

Dingrui Wang, Zhihao Liang, Hongyuan Ye, Zhexiao Sun, Zhaowei Lu, Yuchen Zhang, Yuyu Zhao, Yuan Gao, Marvin Seegert, Finn Rasmus Schäfer, Haotong Qin, Wei Li, Luigi Palmieri, Felix Jahncke, Mattia Piccinini, Johannes Betz

While recent video world models can generate highly real-istic videos, their ability to perform semantic reasoning and planningremains unclear and unquantified. We introduce Target-Bench, thefirst benchmark that enables comprehensive evaluation of video worldmodels’ semantic reasoning, spatial estimation, and planning capabil-ities. Target-Bench provides 450 robot-collected scenarios spanning 47semantic categories, with SLAM-based trajectories serving as motiontendency references. Our benchmark reconstructs motion from generatedvideos with a metric scale recovery mechanism, enabling the evaluationof planning performance with five complementary metrics that focus ontarget-approaching capability and directional consistency. Our evalua-tion result shows that the best off-the-shelf model achieves only a 0.368overall score, revealing a significant gap between realistic visual genera-tion and semantic reasoning in current video world models. Furthermore,we demonstrate that fine-tuning process on a relatively small real-worldrobot dataset can significantly improve task-level planning performance.

ECCV 2026

UniSim-SLAM: Feed-Forward SLAM with Unified Sim(3) Optimization

Inha Lee, Dongjae Jeong, Junhee Lee, Kyungdon Joo

Recent geometric foundation models enable feed-forward in-ference for SLAM, but their predictions are strongly dependent on the in-put view set, which leads to geometric inconsistencies and trajectory driftwhen results are chained over long sequences. Online deployment furtherexposes a trade-off between the low latency of two-view tracking and theconstraint richness of multi-view inference. We introduce UniSim-SLAM,an integrated system that runs lightweight two-view keyframe trackingin the frontend and performs periodic multi-view submap refinementin the backend. To combine predictions defined in heterogeneous localcoordinates with inconsistent scales, we formulate a unified multi-levelfactor graph on Sim(3) that jointly optimizes global keyframe poses andsubmap poses. The graph integrates temporal view-to-view odometryedges, view-to-submap bridge edges with depth-statistics scale anchor-ing, and submap-to-submap tie and scale constraints to enforce consis-tent similarity relations across submaps. Experiments on TUM RGB-Dand 7-Scenes show that UniSim-SLAM achieves state-of-the-art accuracyin the uncalibrated setting, reducing trajectory error by 38.5% on TUMRGB-D and 45.9% on 7-Scenes compared to prior best results. Projectpage: https://vision3d-lab.github.io/unisim-slam/.

ECCV 2026

SVI360: Spherical Video Interpolation

Le-Kim NGUYEN, Renato Martins, Pascal Vasseur, Cedric Demonceaux

This paper addresses the problem of omnidirectional videointerpolation, which plays an essential role in applications such as vir-tual reality and immersive video enhancement. Existing video interpo-lation methods are not well-suited for spherical videos, as they havedifficulty handling severe distortions close to the poles. To address thisissue, we propose SVI360, a dual-branch framework that combines theimage frame and its rotated orthogonal view to deal with these dis-tortions. The core methodological aspect of the approach is to rein-force equivariance of the flow displacements between the original andorthogonal views to improve intermediate frame prediction. Experimentsshow that our method outperforms state-of-the-art approaches in inter-polation quality while maintaining accurate optical flow in four differ-ent public benchmarks. Code and pre-trained models are available at:https://icb-vision-ai.github.io/video360_interpolation/

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