ICCV 2025 Papers

Millimeter-wave radar for state estimation is gaining significant attention for its affordability and reliability in harsh conditions. Existing localization solutions typically rely on post-processed radar point clouds as landmark points. Nonetheless, the inherent sparsity of radar point clouds, ghost points from multi-path effects, and limited angle resolution in single-chirp radar severely degrade state estimation performance. To address these issues, we propose S^3E, a Self-Supervised State Estimator that employs more richly informative radar signal spectra to bypass sparse points and fuses complementary inertial information to achieve accurate localization. S^3E fully explores the association between exteroceptive radar and proprioceptive inertial sensor to achieve complementary benefits. To deal with limited angle resolution, we introduce a novel cross-fusion technique that enhances spatial structure information by exploiting subtle rotational shift correlations across heterogeneous data. The experimental results demonstrate our method achieves robust and accurate performance without relying on localization ground truth supervision. To the best of our knowledge, this is the first attempt to achieve state estimation by fusing radar spectra and inertial data in a complementary self-supervised manner.

The pursuit of a generalizable stereo matching model, capable of performing well across varying resolutions and disparity ranges without dataset-specific fine-tuning, has revealed a fundamental trade-off. Iterative local search methods achieve high scores on constrained benchmarks, but their core mechanism inherently limits the global consistency required for true generalization. However, global matching architectures, while theoretically more robust, have historically been rendered infeasible by prohibitive computational and memory costs. We resolve this dilemma with S2M2: a global matching architecture that achieves state-of-the-art accuracy and high efficiency without relying on cost volume filtering or deep refinement stacks. Our design integrates a multi-resolution transformer for robust long-range correspondence, trained with a novel loss function that concentrates probability on feasible matches. This approach enables a more robust joint estimation of disparity, occlusion, and confidence. S2M2 establishes a new state of the art on Middlebury v3 and ETH3D benchmarks, significantly outperforming prior methods in most metrics while reconstructing high-quality details with competitive efficiency.

The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (ROSS3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, ROSS3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.

Painting textures for existing geometries is a critical yet labor-intensive process in 3D asset generation. Recent advancements in text-to-image (T2I) models have led to significant progress in texture generation. Most existing research approaches this task by first generating images in 2D spaces using image diffusion models, followed by a texture baking process to achieve UV texture. However, these methods often struggle to produce high-quality textures due to inconsistencies among the generated multi-view images, resulting in seams and ghosting artifacts. In contrast, 3D-based texture synthesis methods aim to address these inconsistencies, but they often neglect 2D diffusion model priors, making them challenging to apply to real-world objects. To overcome these limitations, we propose RomanTex, a multiview-based texture generation framework that integrates a multi-attention network with an underlying 3D representation, facilitated by our novel 3D-aware Rotary Positional Embedding. Additionally, we incorporate a decoupling characteristic in the multi-attention block to enhance the model's robustness in image-to-texture task, enabling semantically-correct back-view synthesis. Furthermore, we introduce a geometry-related Classifier-Free Guidance (CFG) mechanism to further improve the alignment with both geometries and images. Quantitative and qualitative evaluations, along with comprehensive user studies, demonstrate that our method achieves state-of-the-art results in texture quality and consistency.

3D Gaussian Splatting (3DGS) has recently emerged as an efficient representation for high-quality 3D reconstruction and rendering. Despite its superior rendering quality and speed, 3DGS heavily relies on the assumption of geometric consistency among input images. In real-world scenarios, violations of this assumption--such as occlusions, dynamic objects, or camera blur--often lead to reconstruction artifacts and rendering inaccuracies. To address these challenges, we introduce RogSplat, a robust framework that leverages generative models to enhance the reliability of 3DGS. Specifically, RogSplat identifies and rectifies occluded regions during the optimization of unstructured scenes. Outlier regions are first detected using our proposed fused features and then accurately inpainted by the proposed RF-Refiner, ensuring reliable reconstruction of occluded areas while preserving the integrity of visible regions. Extensive experiments demonstrate that RogSplat achieves state-of-the-art reconstruction quality on the RobustNeRF and NeRF-on-the-go datasets, significantly outperforming existing methods in challenging real-world scenarios involving dynamic objects.

Vision-Language Models (VLMs) enjoy strong zero-shot performance but are vulnerable to adversarial attacks posing security risks. Adversarially robust fine-tuning enhances zero-shot robustness on new datasets while preserving the natural performance of pre-trained VLMs. However, prior methods use sample-wise adversarial fine-tuning, neglecting the underlying second-order statistics that represent entire groups of samples. This leads to a feature-level discrepancy between clean and adversarial samples of their augmented variants. Thus, we propose to represent groups of samples as subspaces to capture distributions and turn the traditional sample-wise adversarial fine-tuning into its distributional counterpart. For each image, we build distributions from (i) a clean sample with its augmentations and (ii) their adversarial counterparts. For text, we build distributions from (iii) a clean prompt and its synonymous prompts and (iv) their adversarial counterparts. We then perform alignment between image and text subspaces, and "adversarial" subspaces are also aligned toward "clean" subspaces. Thus, all samples underlying these distributions (think infinite number) also get aligned, leading to generalizable robustness. Evaluations on 15 datasets are provided.

3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images. We identify that the Gaussian densification process, while enhancing scene detail capture, unintentionally contributes to these artifacts by growing additional Gaussians that model transient disturbances. To address this, we propose RobustSplat, a robust solution based on two critical designs. First, we introduce a delayed Gaussian growth strategy that prioritizes optimizing static scene structure before allowing Gaussian splitting/cloning, mitigating overfitting to transient objects in early optimization. Second, we design a scale-cascaded mask bootstrapping approach that first leverages lower-resolution feature similarity supervision for reliable initial transient mask estimation, taking advantage of its stronger semantic consistency and robustness to noise, and then progresses to high-resolution supervision to achieve more precise mask prediction. Extensive experiments on multiple challenging datasets show that our method outperforms existing methods, clearly demonstrating the robustness and effectiveness of our method. Our project page is https://fcyycf.github.io/RobustSplat/.

We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize training efficiency. A controllable level-of-detail (LOD) strategy explicitly regulates Gaussian density under a user-defined budget, enabling efficient training and rendering while maintaining high visual fidelity. The appearance transformation module mitigates the negative effects of appearance inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and antialiasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality. The source code is available at: https://yzslab.github.io/REUrbanGS.

High Dynamic Range (HDR) imaging with modulo cameras involves solving a challenging inverse problem, where degradation occurs due to the modulo operation applied to the target HDR image. Existing methods operate directly in the image domain, overlooking the underlying properties of the modulo operation. Motivated by Itoh's continuity condition in optics, we reformulate modulo HDR reconstruction in image gradient domain, leveraging the inherent properties of modulo-wrapped gradients to simplify the problem. Furthermore, to address possible ambiguities on large image gradients, we introduce an auxiliary variable with a learnable sparsity prior in an optimization formulation to absorb the related residuals. This is implemented within an unfolding network, where sparsity is enforced through a spiking neuron-based module. Experiments show that our method outperforms existing approaches while being among the lightest models of existing works.

Gaussian denoising often serves as the initiation of research in the field of image denoising, owing to its prevalence and intriguing properties. However, deep Gaussian denoiser typically generalizes poorly to other types of noises, such as Poisson noise and real-world noise. In this paper, we reveal that deep Gaussian denoisers have an underlying ability to handle other noises with only ten iterations of self-supervised learning, which is referred to as deep denoiser prior. Specifically, we first pre-train a Gaussian denoising model in a self-supervised manner. Then, for each test image, we construct a pixel bank based on the self-similarity and randomly sample pseudo-instance examples from it to perform test-time adaptation. Finally, we fine-tune the pre-trained Gaussian denoiser using the randomly sampled pseudo-instances. Extensive experiments demonstrate that our test-time adaptation method helps the pre-trained Gaussian denoiser rapidly improve performance in removing both in-distribution and out-of-distribution noise, achieving superior performance compared to existing single-image denoising methods while also significantly reducing computational time. Code available at: https://github.com/qingma2016/TTAD.

Recently, multi-view learning (MVL) has garnered significant attention due to its ability to fuse discriminative information from multiple views. However, real-world multi-view datasets are often heterogeneous and imperfect, which usually causes MVL methods designed for specific combinations of views to lack application potential and limits their effectiveness. To address this issue, we propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment. Specifically, we introduce a simple yet effective multi-view transformer fusion network where we transform heterogeneous multi-view data into homogeneous word embeddings, and then integrate multiple views by the sample-level attention mechanism to obtain a fused representation. Furthermore, we propose a simulated perturbation based multi-view contrastive learning framework that dynamically generates the noise and unusable perturbations for simulating imperfect data conditions. The simulated noisy and unusable data obtain two distinct fused representations, and we utilize contrastive learning to align them for learning discriminative and robust representations. Our RML is self-supervised and can also be applied for downstream tasks as a regularization. In experiments, we employ it in multi-view unsupervised clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval. Extensive comparison experiments and ablation studies validate RML's effectiveness. Code is available at https://github.com/SubmissionsIn/RML.

Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing machine unlearning (MU) under data privacy regulations. Existing MU methods designed for full-precision models fail to address two fundamental limitations in quantized networks: 1) Noise amplification from label mismatch during data processing, and 2) Gradient imbalance between forgotten and retained data during training. These issues are exacerbated by quantized models' constrained parameter space and discrete optimization. We propose Q-MUL, the first dedicated unlearning framework for quantized models. Our method introduces two key innovations: 1) Similar Labels assignment replaces random labels with semantically consistent alternatives to minimize noise injection, and 2) Adaptive Gradient Reweighting dynamically aligns parameter update contributions from forgotten and retained data. Through systematic analysis of quantized model vulnerabilities, we establish theoretical foundations for these mechanisms. Extensive evaluations on benchmark datasets demonstrate Q-MUL's superiority over existing approaches.

Synthesizing normal-light novel views from low-light multiview images is an important yet challenging task, given the low visibility and high ISO noise present in the input images. Existing low-light enhancement methods often struggle to effectively preprocess such low-light inputs, as they fail to consider correlations among multiple views. Although other state-of-the-art methods have introduced illumination-related components offering alternative solutions to the problem, they often result in drawbacks such as color distortions and artifacts, and they provide limited denoising effectiveness. In this paper, we propose a novel Robust Low-light Scene Restoration framework (Rose), which enables effective synthesis of novel views in normal lighting conditions from low-light multiview image inputs, by formulating the task as an illuminance transition estimation problem in 3D space, conceptualizing it as a specialized rendering task. This multiview-consistent illuminance transition field establishes a robust connection between low-light and normal-light conditions. By further exploiting the inherent low-rank property of illumination to constrain the transition representation, we achieve more effective denoising without complex 2D techniques or explicit noise modeling. To implement RoSe, we design a concise dual-branch architecture and introduce a low-rank denoising module. Experiments demonstrate that \pname significantly outperforms state-of-the-art models in both rendering quality and multiview consistency on standard benchmarks. The codes and data are available at https://pegasus2004.github.io/RoSe.

Dataset condensation aims to compress large dataset into smaller synthetic set while preserving the essential representations needed for effective model training. However, existing methods show severe performance degradation when applied to noisy datasets. To address this, we present robust dataset condensation (RDC), an end-to-end method that mitigates noise to generate a clean and robust synthetic set, without requiring separate noise-reduction preprocessing steps. RDC refines the condensation process by integrating contrastive learning tailored for robust condensation, named golden MixUp contrast. It uses synthetic samples to sharpen class boundaries and to mitigate noisy representations, while its augmentation strategy compensates for the limited size of the synthetic set by identifying clean samples from noisy training data, enriching synthetic images with real-data diversity. We evaluate RDC against existing condensation methods and a conventional approach that first applies noise cleaning algorithms to the dataset before performing condensation. Extensive experiments show that RDC outperforms other approaches on CIFAR-10/100 across different types of noise, including asymmetric, symmetric, and real-world noise. Code is available at https://github.com/DISL-Lab/RDC-ICCV2025.

Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle highly variable and localized distortions. To address these issue, we propose Spectral-based Spatial Grouping Transformer (SSGformer), a novel approach that leverages spectral decomposition and group-wise attention for multi-weather image restoration. SSGformer decomposes images into high-frequency edge features using conventional edge detection and low-frequency information via Singular Value Decomposition. We utilize multi-head linear attention to effectively model the relationship between these features. The fused features are integrated with the input to generate a grouping-mask that clusters regions based on the spatial similarity and image texture. To fully leverage this mask, we introduce a group-wise attention mechanism, enabling robust adverse weather removal and ensuring consistent performance across diverse weather conditions. We also propose a Spatial Grouping Transformer Block that uses both channel attention and spatial attention, effectively balancing feature-wise relationships and spatial dependencies. Extensive experiments show the superiority of our approach, validating its effectiveness in handling the varied and intricate adverse weather degradations.

Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.

LiDAR-based 3D sensors provide point clouds, a canonical 3D representation used in various scene understanding tasks. Modern LiDARs face key challenges in several real-world scenarios, such as long-distance or low-albedo objects, producing sparse or erroneous point clouds. These errors, which are rooted in the noisy raw LiDAR measurements, get propagated to downstream perception models, resulting in potentially severe loss of accuracy. This is because conventional 3D processing pipelines do not retain any uncertainty information from the raw measurements when constructing point clouds. We propose Probabilistic Point Clouds (PPC), a novel 3D scene representation where each point is augmented with a probability attribute that encapsulates the measurement uncertainty (or confidence) in the raw data. We further introduce inference approaches that leverage PPC for robust 3D object detection; these methods are versatile and can be used as computationally lightweight drop-in modules in 3D inference pipelines. We demonstrate, via both simulations and real captures, that PPC-based 3D inference methods outperform several baselines using LiDAR as well as camera-LiDAR fusion models, across challenging indoor and outdoor scenarios involving small, distant, and low-albedo objects, as well as strong ambient light.

Collecting real-world data for rare high-risk scenarios, long-tailed driving events, and complex interactions remains challenging, leading to poor performance of existing autonomous driving systems in these critical situations. In this paper, we propose RoboTron-Sim that improves real-world driving in critical situations by utilizing simulated hard cases. First, we develop a simulated dataset called Hard-case Augmented Synthetic Scenarios (HASS), which covers 13 high-risk edge-case categories, as well as balanced environmental conditions such as day/night and sunny/rainy. Second, we introduce Scenario-aware Prompt Engineering (SPE) and an Image-to-Ego Encoder (I2E Encoder) to enable multimodal large language models to effectively learn real-world challenging driving skills from HASS, via adapting to environmental deviations and hardware differences between real-world and simulated scenarios. Extensive experiments on nuScenes show that RoboTron-Sim improves driving performance in challenging scenarios by 50%, achieving state-of-the-art results in real-world open-loop planning. Qualitative results further demonstrate the effectiveness of RoboTron-Sim in better managing rare high-risk driving scenarios.