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ECCV 2026

Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models

Wenyue Chen, Wenjue Chen, Peng Li, Qinghe Wang, Xu Jia, Heliang Zheng, Rongfei Jia, Yuan Liu, Ronggang Wang

Recent advancements in 3D generation have significantly en-hanced the fidelity and geometric details of synthesized 3D assets. How-ever, due to the inherent ambiguity of single-view input and the lackof robust global structural priors–caused by limited 3D training data–the unseen regions generated by existing models remain stochastic anddifficult to control. This often results in geometries that are either phys-ically implausible or misaligned with user intent. In this paper, we pro-pose Know3D, a novel framework designed to incorporate rich knowl-edge from Multimodal Large Language Models (MLLMs) into 3D gen-eration processes. By leveraging latent hidden-state injection, Know3Dsupports language-controllable generation of the back-view for 3D as-sets. We utilize a VLM-diffusion-based architecture: the Vision LanguageModel (VLM) is used to provide high-level semantic understanding, whilethe diffusion model serves as a bridge, transferring semantic knowledgeinto the 3D generation model. Extensive experiments demonstrate thatKnow3D effectively bridges the gap between abstract textual instruc-tions and the geometric reconstruction of invisible regions. By trans-forming the traditionally stochastic back-view hallucination into a se-mantically controllable process, Know3D offers a promising direction forhighly plausible and user-friendly 3D generation in the future. Projectpage: https://xishuxishu.github.io/Know3D.github.io/.

ECCV 2026

Intermediate Text Representation Guided Text-to-Image Generation for Enhancing One-and-Only Alignment

Soyoun Won, Aryan Yazdan Parast, Basim Azam, Jean Honorio, Naveed Akhtar

Text-to-image (T2I) diffusion models often fail to faithfullyrender explicit textual descriptions, instead defaulting to strongly learnedvisual priors due to a phenomenon referred to as concept association bias.We show that such bias is particularly strong for one-and-only (OAO)objects, entities that exist in a single canonical form, such as celestialbodies, landmarks, and artworks. The deeply ingrained visual identity forthese concepts often resists modification through prompting alone. Ad-dressing this challenge, we first identify through an information-theoreticanalysis that the final text embedding discards concept-level informationpresent in the intermediate-layer text representations, reducing the mu-tual information available to the subsequent denoising process. We thenpropose Intermediate Text Representation (IR)-guided diffusion, whichinjects intermediate hidden states of the text encoder into the condi-tioning signal during early denoising steps, recovering suppressed con-cepts without any additional training, optimization, or external models.To systematically evaluate the challenging task of aligning generativeoutputs with unusual prompts for OAO objects, we introduce OAO-AttackBench, a benchmark comprising counterfactual prompts that di-rectly conflict with the core visual identity of OAO objects. Experi-ments on four benchmarks, including OAO-AttackBench, show that ourmethod achieves up to a 19.1 percentage-point improvement in VQAS-core while preserving generation fidelity and human preference. Projectpage: https://soyoun-won.github.io/one-and-only-ir-guidance/.

ECCV 2026

HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models

Yu Xue, Haoxuan Qu, Zhuoling Li, Hongbin Xu, Jianxiong Yin, Simon See, Hossein Rahmani, Jun Liu

Training-free text-to-high-resolution image generation has recently attracted growing research attention. However, existing studies on this task primarily focus on adapting off-the-shelf U-Net-based diffusion models to high resolutions, with limited progress on adapting off-the-shelf Diffusion Transformer (DiT) models despite their strong text-to-image generation capabilities at limited resolutions. In this work, we find two key challenges particularly hindering the application of offthe-shelf DiT models for high-resolution image synthesis in a training-free manner, namely, spatial disorder and long generation time. To address these challenges, we propose a novel method tailored to adapt off-theshelf DiT models for high-resolution image synthesis. Extensive experiments show the efficacy of our method.

ECCV 2026

Group3D: MLLM-Guided Semantic Grouping for Open-Vocabulary 3D Object Detection

Youbin Kim, Jinho Park, Hogun Park, Eunbyung Park

Open-vocabulary 3D object detection aims to localize andrecognize objects beyond a fixed training taxonomy. In multi-view RGBsettings, recent approaches often decouple geometry-based instance con-struction from semantic labeling, generating class-agnostic fragments andassigning open-vocabulary categories post hoc. While flexible, such de-coupling leaves instance construction governed primarily by geometricconsistency, without semantic constraints during merging. When geomet-ric evidence is view-dependent and incomplete, this geometry-only merg-ing can lead to irreversible association errors, including over-mergingof distinct objects or fragmentation of a single instance. We proposeGroup3D, a multi-view open-vocabulary 3D detection framework thatintegrates semantic constraints directly into the instance constructionprocess. Group3D maintains a scene-adaptive vocabulary derived from amultimodal large language model (MLLM) and organizes it into semanticcompatibility groups that encode plausible cross-view category equiva-lence. These groups act as merge-time constraints: 3D fragments are asso-ciated only when they satisfy both semantic compatibility and geometricconsistency. This semantically gated merging mitigates geometry-drivenover-merging while absorbing multi-view category variability. Group3Dsupports both pose-known and pose-free settings, relying only on RGBobservations. Experiments on ScanNet and ARKitScenes demonstratethat Group3D achieves state-of-the-art performance in multi-view open-vocabulary 3D detection, while exhibiting strong generalization in zero-shot scenarios. The project page is available at https://ubin108.github.io/Group3D/.

ECCV 2026

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for Efficient HDR Reconstruction

Tao Hu, Weiyu Zhou, Yanjie Tu, Wei Dong, Peng Wu, Qingsen Yan, Yanning Zhang

Pre-trained Latent Diffusion Models (LDMs) have recentlyshown strong perceptual priors for low-level vision tasks, making thema promising direction for multi-exposure High Dynamic Range (HDR)reconstruction. However, directly applying LDMs to HDR remains chal-lenging due to: (1) limited dynamic-range representation caused by 8-bitlatent compression, (2) high inference cost from multi-step denoising,and (3) content hallucination inherent to generative nature. To addressthese challenges, we introduce GMODiff, a gain map-driven one-stepdiff usion framework for multi-exposure HDR reconstruction. Instead ofreconstructing full HDR content, we reformulate HDR reconstructionas a degradation-aware Gain Map (GM) refinement problem, where theGM encodes the extended dynamic range while retaining the same bitdepth as LDR images. We initialize the denoising process from an in-formative regression-based estimate rather than pure noise, allowing themodel to generate high-quality GMs in a single denoising step. Further-more, recognizing that regression-based models excel in content fidelitywhile LDMs favor perceptual quality, we leverage regression priors toguide both the denoising process and latent decoding of the LDM, sup-pressing hallucinations while preserving structural accuracy. Extensiveexperiments demonstrate that our GMODiff performs favorably againstseveral state-of-the-art methods and is 100× faster than previous LDM-based methods.

ECCV 2026

Generative Refinement Network for Visual Synthesis

Jian Han, Jinlai Liu, Jiahuan Wang, BINGYUE PENG, Zehuan Yuan

While diffusion models dominate the field of visual genera-tion, they remain computationally inefficient, as they allocate uniformcomputational effort to samples with varying levels of complexity. In con-trast, autoregressive (AR) models are inherently complexity-aware, asevidenced by their variable likelihoods, but are often hindered by lossydiscrete tokenization and error accumulation. In this work, we introduceGenerative Refinement Networks (GRN), a next-generation visual syn-thesis paradigm to address these issues. At its core, GRN addresses thediscrete tokenization bottleneck through a theoretically near-lossless Hi-erarchical Binary Quantization (HBQ), achieving a reconstruction qual-ity comparable to continuous counterparts. Built upon HBQ’s latentspace, GRN fundamentally upgrades AR generation with a global re-finement mechanism that progressively perfects and corrects artworks —like a human artist painting. Besides, GRN integrates an entropy-guidedsampling strategy, enabling complexity-aware, adaptive-step generationwithout compromising visual quality. On the ImageNet benchmark, GRNestablishes new records in image reconstruction (0.56 rFID) and class-conditional image generation (1.81 gFID). We also scale GRN to morechallenging text-to-image and text-to-video generation, delivering supe-rior performance on an equivalent scale. We release all models and codeto foster further research on GRN.

ECCV 2026

Generalization and Memorization in Rectified Flow

Mingxing Rao, Daniel Moyer

Generative models based on the Flow Matching objective,particularly Rectified Flow, have emerged as a dominant paradigm forefficient, high-fidelity image synthesis. However, while existing researchheavily prioritizes generation quality and architectural scaling, the un-derlying dynamics of how RF models memorize training data remainlargely underexplored. In this paper, we systematically investigate thememorization behaviors of RF through the test statistics of MembershipInference Attacks (MIA). We progressively formulate three test statistics,culminating in a complexity-calibrated metric (Tmc_cal ) that successfullydecouples intrinsic image spatial complexity from genuine memorizationsignals. This calibration yields a significant performance surge—boostingattack AUC by up to 15% and the privacy-critical TPR@1%FPR metricby up to 45%—establishing the first non-trivial MIA specifically tailoredfor RF. Leveraging these refined metrics, we uncover a distinct temporalpattern: under standard uniform temporal training, a model’s suscepti-bility to MIA strictly peaks at the integration midpoint, a phenomenonwe justify via the network’s forced deviation from linear approximations.Finally, we demonstrate that substituting uniform timestep samplingwith a Symmetric Exponential (U-shaped) distribution effectively min-imizes exposure to vulnerable intermediate timesteps. Extensive evalu-ations across three datasets confirm that this temporal regularizationsuppresses memorization while preserving generative fidelity.

ECCV 2026

GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models

Jiaxin Zhang, Junjun Jiang, Haijie Li, Youyu Chen, Kui Jiang, Dave Zhenyu Chen

Multimodal Large Language Models (MLLMs) demonstrateexceptional semantic reasoning but struggle with 3D spatial perceptionwhen restricted to pure RGB inputs. Despite leveraging implicit geo-metric priors from 3D reconstruction models, image-based methods stillexhibit a notable performance gap compared to methods using explicit3D data. We argue that this gap does not arise from insufficient geo-metric priors, but from a misalignment in the training paradigm: text-dominated fine-tuning fails to activate geometric representations withinMLLMs. Existing approaches typically resort to naive feature concate-nation and optimize directly for downstream tasks without geometry-specific supervision, leading to suboptimal structural utilization. To ad-dress this limitation, we propose GAP-MLLM, a Geometry-AlignedPre-training paradigm that explicitly activates structural perception be-fore downstream adaptation. Specifically, we introduce a visual-promptedjoint task that compels the MLLMs to predict sparse pointmaps along-side semantic labels, thereby enforcing geometric awareness. Further-more, we design a multi-level progressive fusion module with a token-level gating mechanism, enabling adaptive integration of geometric priorswithout suppressing semantic reasoning. Extensive experiments demon-strate that GAP-MLLM significantly enhances geometric feature fusionand consistently enhances performance across 3D visual grounding, 3Ddense captioning, and 3D video object detection tasks.

ECCV 2026

Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection

Peng Zhang, Tingfa Xu, Shuaihao Han, Jianan Li

Existing multispectral detectors are limited by discrete spec-tral processing, a scale-dependent shift in the relative reliability of spec-tral and spatial cues across pyramid levels, and the lack of explicitrotation-equivariant geometric priors for arbitrarily oriented objects. Totackle these limitations, we propose FressDet, a fully rotation-equivariantspectral_x0015_spatial learning framework for multispectral object detection,capable of capturing the continuous, ordered nature of spectral struc-ture and enabling reliable spectral_x0015_spatial fusion across pyramid levelsunder arbitrary in-plane rotations. FressDet integrates three complemen-tary components. Spectral Implicit Warp (SpeIW) enables query-basedspectral resampling via a coordinate-conditioned implicit _x001C_eld, yieldinga monotone, order-preserving warp. Rotation-Equivariant ConsistencyWeighting (ReCoW) adaptively fuses spectral and spatial branches basedon branch reliability, reinforcing informative cues while suppressing noiseacross pyramid levels. The oriented-aware head exploits group-indexedfeatures to stably predict oriented objects without parameter replication.Taken together, FressDet learns more discriminative and robust spectral_x0015_spatial representations even under rotational perturbations. By achievingstate-of-the-art performance with 93% fewer parameters on _x001C_ve publicbenchmarks, FressDet demonstrates its e_x001B_ectiveness and generalizability.Code is available at https://github.com/Riiluo/FressDet.

ECCV 2026

FSD-Net: Foundation-Guided Spatiotemporal Distillation for Video Polyp Segmentation

Yahang Leng, Shiquan Min, Chengzhou Li

Video polyp segmentation plays a pivotal role in early col-orectal cancer diagnosis, yet its clinical deployment is heavily bottle-necked by the prohibitive cost of dense pixel-level annotations. To addressthis challenge, we propose a foundation-guided spatiotemporal distilla-tion framework named FSD-Net for sparsely annotated video polyp seg-mentation. Our approach introduces a Semantic Flow Distillation (SFD)module that leverages a frozen foundation model as a teacher to extractrobust semantic features and generate continuous supervision signals fora lightweight student network. To tackle the frequent target disappear-ance caused by complex intestinal folds, we design an Occlusion-AwareCycle Consistency (OACC) mechanism that dynamically blocks erro-neous gradient propagation based on visibility estimation. Furthermore,we introduce a Prototype Re-identification Module (PRM) to maintainlong-term memory and successfully recapture target identities upon reap-pearance. Extensive experiments on three standardized datasets, includ-ing SUN-SEG, CVC-612, and CVC-300, demonstrate that our frame-work significantly outperforms existing weakly supervised methods andeven surpasses state-of-the-art fully supervised models. Notably, on thehighly challenging SUN-SEG-Hard dataset, our method achieves a mDice87.04%, mIoU of 79.20%, and mHD of 20.64 mm, proving its exceptionalrobustness and label efficiency in complex endoscopic environments.

ECCV 2026

FoundYou: A Unified Model for Personalized Segmentation and Retrieval

Gabriele Trivigno, Marcos Alfaro Perez, Claudia Cuttano, Gabriele Berton, Luis Payá, Carlo Masone

Personalized segmentation and personalized retrieval bothaim to identify the same physical object across different images. Whilethe former localizes the object within a target image, the latter retrievesimages where it appears. Despite this shared instance-level objective, thetwo tasks have largely evolved separately and are addressed with distinctsolutions. In this work, we introduce FoundYou, a unified framework builton the observation that Segment Anything 2 (SAM 2), trained to pre-serve object identity across video frames, inherently captures instance-level cues. We leverage this property to match objects across independentimages, enabling segmentation and retrieval to emerge as two outcomesof the same instance alignment process. This unified view unlocks newcapabilities beyond traditional benchmarks, including few-shot person-alized retrieval and promptable personalized segmentation with flexibleprompts. Extensive experiments show consistent gains over unified andtask-specific methods, including +18.4 mIoU on PerMIS and +17.8 mAPon ILIAS. Performance scales with additional references and remainsrobust to weaker prompts. Beyond personalization, FoundYou achievesstate-of-the-art results on category-level retrieval benchmarks. Notably,our approach keeps the SAM 2-small model entirely frozen and addsonly 5.9 M trainable parameters, yielding a 52 M-parameter model thatis over 75× faster and 20× smaller than the only prior unified solution.

ECCV 2026

Follow-Your-Mind: Towards Inversion-Free Brain-Driven Visual Context Synthesis and Editing

Haodong Jing, Panqi Yang, Rongchao Zhang, Zhipeng Liu, Yajun Liu, Xuehai Bai, Yongqiang Ma, Nanning Zheng

Deciphering human visual intent and manipulating visual con-tent via brain signals represents a significant milestone in neuro-generativeAI. Recent studies have demonstrated impressive image reconstructionfrom brain activity, yet they often rely on slow iterative diffusion inversionprocesses, makes it difficult to achieve real-time editing and fails to unifygeneration with precise context modification. Rectified Flow has recentlyemerged as a powerful solution for fast, deterministic generation. Inspiredby this, we propose MinD-Flow, a pioneering inversion-free framework forunified brain-driven synthesis and editing. It incorporates a Neuro-awareEncoder to achieve unified encoding of heterogeneous brain signals. Com-bined with the Neuro-Perceiver Bridge (NPB), it aligns brain semanticswith visual anchors, mapping the ‘Mind Space’ to the generative latentspace. Furthermore, the Decoupled Flow Guidance (DFG) and AdaptiveSoft Masking overcome the conflict between structural preservation andsemantic changes, enabling precise local editing without noise inversion.We introduce Brain-Gen Benchmark to standardize the evaluation ofNeuro-context/creation/editing tasks. Comprehensive experiments showSOTA performance, providing a versatile tool for BCI applications.

ECCV 2026

FlowFace: Rectifying Identity Conditioning with Riemannian Geometry for Face Generation

Xinran Deng, Yiling Wu, Ye Tian, Libo Zhang

Diffusion-based face generation commonly combines iden-tity cues and edit conditions through Euclidean mixing at the condi-tioning interface. This becomes fragile when facial attributes are cou-pled: naively adding feature vectors ignores their interactions and canlead to identity drift and artifacts. We present FlowFace, a geometry-aware identity-conditioning framework that rectifies the Stable Diffusionconditioning geometry during training without modifying the inferencesampler. FlowFace includes (i) a dual-stream manifold encoder inspiredby fiber bundles to disentangle geometry-related variation from seman-tic identity, (ii) a Lie-algebraic composition module based on a trun-cated BCH expansion to introduce an explicit interaction term betweencoupled edits, and (iii) a geodesic-consistency regularizer that learns alocal SPD metric that encourages Euclidean operations to better cor-relate with a geodesic-consistent surrogate under the learned geome-try. Experiments show that FlowFace outperforms strong baselines inidentity preservation, text alignment, and perceptual quality, while re-taining adapter-level inference efficiency. The code will be available athttps://github.com/SunFly0/Flowface.

ECCV 2026

Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints

Yu Qi, Xinyi Xu, Ziyu Guo, Siyuan Ma, Renrui Zhang, Xinyan Chen, Ruichuan An, Ruofan Xing, Jiayi Zhang, Haojie Huang, Pheng-Ann Heng, Jonathan Tremblay, Lawson Wong

Video generative models show emerging reasoning behaviors.It is essential to ensure that generated events remain causally consistentacross frames for reliable deployment, a property we define as reasoningcoherence. To bridge the gap in literature for missing reasoning coher-ence evaluation, we propose MME-CoF-Pro, a comprehensive videoreasoning benchmark to assess reasoning coherence in video models.Specifically, MME-CoF-Pro contains 303 samples across 16 categories,ranging from visual logical to scientific reasoning. It introduces Rea-soning Score as evaluation metric for assessing process-level necessaryintermediate reasoning steps, and includes three evaluation settings, (a)no hint, (b) text hint, and (c) visual hint, enabling a controlled investi-gation into the underlying mechanisms of reasoning hint guidance. Eval-uation results in 7 open and closed-source video models reveals insightsincluding: (1) Video generative models exhibit weak reasoning coherence,decoupled from generation quality. (2) Text hints boost apparent correct-ness but often cause inconsistency and hallucinated reasoning (3) Visualhints benefit structured perceptual tasks but struggle with fine-grainedperception. We provide our project page along with data and code at:https://video-reasoning-coherence.github.io/.

ECCV 2026

FEEL (Force-Enhanced Egocentric Learning): A Dataset for Physical Action Understanding

Eadom Dessalene, Botao He, Michael Maynord, Yonatan Tussa, Pavan Mantripragada, Yianni Karabatis, Nirupam Roy, Yiannis Aloimonos

We introduce FEEL (Force-Enhanced Egocentric Learning), the first large-scale dataset pairing force measurements gathered from custom piezoresistive gloves with egocentric video. Our gloves enable scalable data collection, and FEEL contains approximately 2 million force-synchronized frames of natural unscripted manipulation in kitchen environments, with ∼45% of frames involving hand-object contact. Because force is the underlying cause that drives physical interaction, it is a critical primitive for physical action understanding. We demonstrate the utility of force for physical action understanding through application of FEEL to two families of tasks: (1) contact understanding, where we jointly perform temporal contact segmentation and pixel-level contacted object segmentation; and, (2) action representation learning, where force prediction serves as a self-supervised pretraining objective for video backbones. We achieve state-of-the-art temporal contact segmentation results and competitive pixel-level segmentation results without any need for manual contacted object segmentation annotations. Furthermore we demonstrate that action representation learning with FEEL improves transfer performance on action understanding tasks without any manual labels over EPIC-Kitchens, SomethingSomething-V2, EgoExo4D and Meccano.

ECCV 2026

EVAR: Edge Visual Autoregressive Models via Principled Pruning

Zefang Wang, Ying Li, Yanyu Li, Mingluo Su, Simin Xu, Guanzhong Tian, Huan Wang

Next-scale visual autoregressive (VAR) models offer stronggenerative fidelity but remain computationally prohibitive for resource-constrained edge devices. We introduce EVAR, a structured-pruningframework tailored to next-scale VAR models. We find that standardpruning paradigms are poorly matched to multi-scale VAR architec-tures: rapid token growth at later scales dominates Hessian accumu-lation, marginalizing critical coarse scales and amplifying autoregres-sive error cascading. To address this issue, EVAR introduces Pyramid-Aware Optimal Brain Surgeon. Through pyramid-aware Hessian accu-mulation, Pyramid-Aware OBS rebalances scale-wise activation statisticsand biases the solver toward preserving coarse-scale representations whilemaintaining an invertible Hessian estimate for closed-form weight com-pensation. Additionally, we propose Progressive Scale-Aware Distillation(PSAD) to counteract scale-wise gradient imbalance during fine-tuning.On ImageNet benchmarks, EVAR substantially reduces parameters andmodel footprint while retaining competitive quality. On an iOS deploy-ment, EVAR further cuts single-image latency from 494 ms to 277 ms(1.8× speedup), with FID changing only marginally.

ECCV 2026

Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models

Marcel Gröpl, Jaewoo Jung, Seungryong Kim, Marc Pollefeys, Sunghwan Hong

Despite rapid progress, pretrained vision–language models still struggle when answers depend on tiny visual details or on combining clues spread across multiple regions, as in documents and compositional queries. We address this by framing grounding as test-time evidence retrieval: given a query, the model should actively identify where to look next to resolve ambiguity. To this end, we propose a training-free, model-intrinsic grounding method that uses uncertainty as supervision. Specifically, we compute the entropy of the model’s next-token distribution and backpropagate it to the visual token embeddings to obtain an entropy-gradient relevance map, without auxiliary detectors or attentionmap heuristics. We then extract and rank multiple coherent regions to support multi-evidence queries, and introduce an iterative zoom-andreground procedure with a spatial-entropy stopping rule to avoid over-refinement. Experiments on seven benchmarks across four VLM architectures demonstrate consistent improvements over existing methods, with the largest gains on detail-critical and high-resolution settings, while also producing more interpretable evidence localizations.

ECCV 2026

EgoGVAE: Ego-body Mesh Reconstruction via Guided Variational Autoencoder

Jaehun Jung, Wonjun Kim

We address the problem of recovering the full-body meshfrom only the head pose. This task has become essential for variousapplications based on head-mounted devices or smart glasses. The chal-lenge of this task lies in estimating the pose information of unobservedbody parts based solely on a single joint (i.e., head) trajectory. Severalstudies have begun to adopt head-conditioned generative models, how-ever, such previous methods are costly and time-consuming due to thediffusion-based iterative process. As an alternative, we propose a simpleyet novel method that leverages the latent space of the guidance network,which is designed as a variational autoencoder taking full-body poses asinputs. By enforcing latent distributions of this guidance network andour head-to-motion network to be similar, latent features sampled fromthe ‘guided’ distribution, i.e., distribution learned in our head-to-motionnetwork, can be reliably decoded for natural representations of full-bodyposes even only with the head pose. One important advantage of theproposed method is that one-step sampling scheme achieves remark-ably fast inference (more than 50 times faster) compared to diffusion-based approaches. Experimental results on benchmark datasets showthat the proposed method efficiently improves the performance of ego-body mesh reconstruction. The code and model are publicly available at:https://github.com/DCVL-3D/EgoGVAE_release.

ECCV 2026

Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving

Adwait Chandorkar, Kai Krink, Yerdana Maulenbay, Hasan Tercan, Tobias Meisen

Recent advancements in LiDAR-only 3D object detectionhave demonstrated improved detection accuracy over benchmark datasets.However, the adversarial robustness of these models remains untested.Very few adversarial robustness studies exist for LiDAR-only 3D ob-ject detection and unfortunately, even they are limited to legacy models.Moreover, there is a systemic gap in the existing evaluation frameworksthat rely simply on mAP ignoring other structural and predictive factors.To fill this gap, we propose a holistic framework that evaluates adversar-ial robustness using two structural factors (point cloud density and pointcloud localization) and three predictive factors (misclassification, local-ization error, distance from ego). Using this framework, we perform anempirical study and critical analysis on recent and legacy state-of-the-art models using adversarial attacks specifically designed for LiDAR-based models. Our key finding is that high-capacity, voxel-based detec-tors are more susceptible to structured coordinate perturbations thanpillar-based detectors. Additionally, non-anchor-based detectors demon-strate poor adversarial robustness, which necessitates rethinking modeltraining techniques. Overall, our results demonstrate that recent modelsare as vulnerable to adversarial attacks as their predecessors. Therefore,we argue that there is a need to improve the evaluation benchmarksfor 3D object detection that not only reward architectural modificationsfor improving detection accuracy, but also evaluate whether the designchoices improve adversarial robustness.

ECCV 2026

CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

Zhihao Yang, Zhiyu Xiang, Peng Xu, Tianyu Pu, Kai Wang, Eryun Liu, Dongping Zhang, Yong Ding

V2X collaborative object detection features overcoming thelimitations of single-vehicle systems by aggregating environmental fea-tures from multiple collaborative agents. However, existing mainstreamV2X perception methods mainly focus on 2D BEV object detection.When 3D detection task is concerned, inferior results are obtained be-cause they ignore the 3D spatial misalignment caused by differing heightand attitude among the collaborators. In this paper, we propose a novelcollaborative 3D object detection framework called CoGoal3D, whichextracts and refines the 3D feature gradually in a two-stage pipeline. Inthe first stage, a multiscale 3D-aware global fusion module is designedto mitigate the 3D spatial misalignment. The resulting proposals arethen refined in the second stage with an auxiliary task of 3D point re-construction. An effective multi-agent collaborative data augmentationstrategy is further proposed to enrich the training data while minimizinginformation loss. Extensive experiments on public real-world datasetsdemonstrate that our CoGoal3D achieves new state-of-the-art perfor-mance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18%on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively.

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