The growing demand for immersive 3D content calls for automated monocular-to-stereo video conversion. We present a controllable, direct end-to-end method for upgrading a conventional video to a binocular one. Our approach, based on (conditional) latent diffusion, avoids artifacts due to explicit depth estimation and warping. The key to its high-quality stereo video output is a novel, guided VAE decoder that ensures sharp and epipolar-consistent stereo video output. Moreover, our method gives the user control over the strength of the stereo effect (respectively, the disparity range) at inference time, via an intuitive, scalar tuning knob. Experiments on three different datasets of real-world stereo videos show that our method outperforms both traditional warping-based and recent warping-free baselines and sets a new standard for reliable, controllable stereo video conversion. Please check the project page for the video samples: elastic3d.github.io
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Monocular Semantic Scene Completion (SSC) aims to reconstruct complete 3D semantic scenes from a single RGB image, offering a cost-effective solution for autonomous driving and robotics. However, the inherently imbalanced nature of voxel distributions--where over 93% of voxels are empty and foreground classes are rare--poses significant challenges. Existing methods often suffer from redundant emphasis on uninformative voxels and poor generalization to long-tailed categories. To address these issues, we propose VoxSAMNet (Voxel Sparsity-Aware Modulation Network), a unified framework that explicitly models voxel sparsity and semantic imbalance. Our approach introduces: (1) a Dummy Shortcut for Feature Refinement (DSFR) module that bypasses empty voxels via a shared dummy node while refining occupied ones with deformable attention; (2) a Foreground Modulation Strategy combining Foreground Dropout (FD) and Text-Guided Image Filter (TGIF) to alleviate overfitting and enhance class-relevant features. Extensive experiments on the public benchmarks SemanticKITTI and SSCBench-KITTI-360 demonstrate that VoxSAMNet achieves state-of-the-art performance, surpassing prior monocular and stereo baselines with mIoU scores of 18.2% and 20.2%, respectively. Our results highlight the importance of sparsity-aware and semantics-guided design for efficient and accurate 3D scene completion, offering a promising direction for future research.
While iterative stereo matching achieves high accuracy, its dependence on Recurrent Neural Networks (RNN) hinders edge deployment, a challenge underexplored in existing researches. We analyze iterative refinement and reveal that disparity updates are spatially sparse and temporally redundant. First, we introduce a progressive iteration pruning strategy that suppresses redundant update steps, effectively collapsing the recursive computation into a near-single-pass inference. Second, we propose a collaborative monocular prior transfer framework that implicitly embeds depth priors without requiring a dedicated monocular encoder, thereby eliminating its associated computational burden. Third, we develop FlashGRU, a hardware-aware RNN operator leveraging structured sparsity and I/O-conscious design, achieving a 7.28xspeedup, 76.6% memory peak reduction and 80.9% global memory requests reduction over natvie ConvGRUs under 2K resolution. Our PipStereo enables real-time, high-fidelity stereo matching on edge hardware: it processes 320x640 frames in just 75ms on an NVIDIA Jetson Orin NX (FP16) and 19ms on RTX 4090, matching the accuracy of large iterative based models, and our generalization ability and accuracy far exceeds that of existing real-time methods.
CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography
PDF ↗Autonomous driving must operate reliably across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. Moreover, recent driving datasets primarily provide sparse LiDAR ground truth for images, which is insufficient for assessing fine-grained geometry in depth estimation and completion. To address these gaps, we introduce CARD, a multi-modal driving dataset that delivers quasi-dense 3D ground truth across continuous sequences rich in speed bumps, potholes, irregular surfaces and off-road segments. Our sensor suite includes synchronized global-shutter stereo cameras, front and rear LiDARs, 6-DoF poses from LiDAR-inertial odometry, per-wheel motion traces, and full calibration. Notably, our multi-LiDAR fusion yields 500K valid depth pixels per frame, about 6.5x more than KITTI Depth Completion and 10x more on average than other public driving datasets. The dataset spans 110 km and 4.7 hours across Germany and Italy. In addition, CARD provides 2D bounding boxes targeting road-topography irregularities, enabling accurate benchmarking for both geometry and perception tasks. Furthermore, we introduce a standardized evaluation protocol for road surface irregularities and a stereo-guided depth completion variant that achieves leading performance on CARD. Moreover, we benchmark state-of-the-art depth estimation models to establish strong baselines. We host CARD on Hugging Face with an open source SDK and standardized splits to enable public leaderboards and reproducible evaluation. The CARD dataset is hosted on (https://huggingface.co/CARD-Data).
Learning-based Multi-View Stereo (MVS) methods have become the mainstream in the field, relying on the construction of cost Learning-based Multi-View Stereo (MVS) methods have become the mainstream in the field, relying on the construction of cost volumes through multi-view feature similarity computation. However, existing methods depend heavily on photometric consistency across views, leading to poor performance in challenging regions. To overcome this limitation, we propose SPE-MVS, a novel MVS framework enhanced with Spatial Position Encoding (SPE). The SPE represents the 3D positional information of pixels in each image within a unified metric space, constructed using monocular depth priors. We integrate the SPE alongside image data as input and introduce a Photometric-Spatial Hybrid Feature Extractor, along with an SPE-enhanced cost volume construction module. These components incorporate spatial position-based similarity computation, substantially improving robustness in challenging areas. Furthermore, we propose a Monocular Depth-guided Enhancement (MDGE) module that enhances depth probability map using monocular depth priors, thereby further boosting the depth estimation performance. Extensive experiments demonstrate that our method significantly improves reconstruction quality in difficult regions and achieves state-of-the-art (SOTA) performance on multiple benchmarks. The code will be released at https://github.com/bdwsq1996/SPE-MVS.
Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Event cameras offer an alternative visual representation with higher dynamic range free from such limitations. The complementary characteristics of the two modalities make event-frame asymmetric stereo promising for reliable 3D perception under fast motion and challenging illumination. However, the modality gap often leads to marginalization of domain-specific cues essential for cross-modal stereo matching. In this paper, we introduce Bi-CMPStereo, a novel bidirectional cross-modal prompting framework that fully exploits semantic and structural features from both domains for robust matching. Our approach learns finely aligned stereo representations within a target canonical space and integrates complementary representations by projecting each modality into both event and frame domains. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in accuracy and generalization.
Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have become crucial components of text-to-image (T2I) generation systems where they are used at various stages for dataset filtering, as evaluation metrics, as a supervisory signal during optimization of parameters, and for post-generation safety and quality filtering of T2I outputs. While specific problems with the integration of RMs into the T2I pipeline have been studied (e.g. reward hacking or mode collapse), their robustness and fairness as scoring functions remains largely unknown. We conduct a large scale audit of RM robustness with respect to demographic biases during T2I model training and generation. We provide quantitative and qualitative evidence that while originally developed as quality measures, RMs encode demographic biases, which cause reward-guided optimization to disproportionately sexualize female image subjects, reinforce gender/racial stereotypes, and collapse demographic diversity. These findings highlight shortcomings in current reward models, challenge their reliability as quality metrics, and underscore the need for improved data collection and training procedures to enable more robust scoring.
Synthetic datasets are a crucial ingredient for training stereo matching networks, but the question of what makes a stereo dataset effective remains underexplored. We investigate the design space of synthetic datasets by varying the parameters of a procedural dataset generator, and report the effects on zero-shot stereo matching performance using standard benchmarks. We validate our findings by collecting the best settings and creating a large-scale dataset. Training only on this dataset achieves better performance than training on a mixture of widely used datasets, and is competitive with training on the FoundationStereo dataset, with the additional benefit of open-source generation code and an accompanying parameter analysis to enable further research. We open-source our system to enable further research on procedural stereo datasets.
We present StereoWorld, a camera-conditioned stereo world model that jointly learns appearance and binocular geometry for end-to-end stereo video generation.Unlike monocular RGB or RGBD approaches, StereoWorld operates exclusively within the RGB modality, while simultaneously grounding geometry directly from disparity. To efficiently achieve consistent stereo generation, our approach introduces two key designs: (1) a unified camera-frame RoPE that augments latent tokens with camera-aware rotary positional encoding, enabling relative, view- and time-consistent conditioning while preserving pretrained video priors via a stable attention initialization; and (2) a stereo-aware attention decomposition that factors full 4D attention into 3D intra-view attention plus horizontal row attention, leveraging the epipolar prior to capture disparity-aligned correspondences with substantially lower compute. Across benchmarks, StereoWorld improves stereo consistency, disparity accuracy, and camera-motion fidelity over strong monocular-then-convert pipelines, achieving more than 3x faster generation with an additional 5% gain in viewpoint consistency. Beyond benchmarks, StereoWorld enables end-to-end binocular VR rendering without depth estimation or inpainting, enhances embodied policy learning through metric-scale depth grounding, and is compatible with long-video distillation for extended interactive stereo synthesis.
FreqSIC: Frequency-aware Stereo Image Compression with Bi-directional Checkerboard Context Model
PDF ↗Stereo image compression is essential for a wide range of 3D vision. Recent methods have demonstrated strong capabilities in eliminating inter-view redundancy and enabling compact entropy coding via spatial-domain stereo transformation and advanced autoregressive entropy models. However, these approaches often suffer from high-frequency information loss and incur considerable coding latency. To overcome these limitations, we propose a novel frequency stereo context transfer (FSCT) module. Unlike spatial-domain methods, the FSCT module separately captures inter-view redundancy in high- and low-frequency components and dynamically balances their contributions to preserve reconstruction quality. In addition, we replace the conventional autoregressive framework with a checkerboard strategy and integrate the FSCT module to model inter-view priors, enabling faster and more efficient entropy coding. Extensive experiments demonstrate that our method achieves state-of-the-art rate-distortion performance among existing stereo image compression approaches, while also attaining the lowest coding latency.
3D Gaussian splatting (3DGS) has emerged as a revolutionary scene representation in simultaneous localization and mapping (SLAM) research. However, existing research on 3DGS-based SLAM fails to accurately address the appearance variations induced by camera auto-exposure in prevalent real-world scenarios, resulting in reduced localization and photorealistic mapping accuracy. To address this issue, we propose a stereo auto-exposure-robust Gaussian splatting SLAM (AERGS-SLAM), a framework robust to such variations and enables both reliable localization and exposure-controlled photorealistic mapping. Our key contributions are two fold. Firstly, we propose a camera exposure network to model the camera exposure process, which we integrate with Gaussian splatting to achieve exposure-controlled novel view synthesis. Secondly, we exploit an illumination-robust geometric feature for localization and Gaussian map initialization, enhancing localization accuracy under exposure-varying scenarios. Extensive experiments on public datasets and our self-collected real-world dataset demonstrate that AERGS-SLAM outperforms baselines in both localization performance and photorealistic mapping quality. Our code is available at: https://github.com/zzy-2021/AERGS-SLAM.
High-resolution and high-precision digital elevation models (DEMs) of the lunar surface are essential for landing site selection and geological research. However, traditional stereo matching provides a limited representation of the 3D scene and struggling with non-textured regions and extreme illumination variations. Recent lunar neural rendering methods are also ill-suited for 3D reconstruction due to their reliance on simple pinhole approximations for pushbroom sensors. These challenges are further compounded by geometric misalignment, distributional bias, and labor-intensive handcrafted preprocessing in satellite image pipelines. To address these issues, we introduce the Lunar Neural Elevation Model (LNEM), a volumetric reconstruction method that explicitly incorporates the pushbroom imaging process. A core component of our approach is Lunar Studio, a multi-orbit dataset and pipeline constructed using Rigorous Sensor Models (RSMs) to produce geometrically consistent observations from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and the Korea Pathfinder Lunar Orbiter (KPLO) Lunar Terrain Imager (LUTI). LNEM integrates this pushbroom camera formulation with learned shadow modeling, enabling geometrically grounded and illumination-aware volumetric rendering under challenging lunar lighting conditions. Extensive experiments demonstrate that LNEM achieves geometrically consistent reconstruction across multiple sensors under diverse viewing and illumination conditions, providing a scalable complement to conventional DEM pipelines. To support reproducibility and future lunar research, we release Lunar Studio, the multi-orbit dataset, and the LNEM reconstruction pipeline.
AutoDebias: An Automated Framework for Detecting and Mitigating Backdoor Biases in Text-to-Image Models
PDF ↗Text-to-Image (T2I) models generate high-quality images but are vulnerable to malicious backdoor attacks that inject harmful biases (e.g., trigger-activated gender or racial stereotypes). Existing debiasing methods, often designed for natural statistical biases, struggle with these deliberate and subtle injected attacks. We propose AutoDebias, a framework that automatically identifies and mitigates these malicious biases in T2I models without prior knowledge of the specific attack vectors. Specifically, AutoDebias leverages vision-language models to detect trigger-activated visual patterns and constructs neutralization guides by generating counter-prompts. These guides drive a CLIP-guided training process that breaks the harmful associations while preserving the original model's image quality and diversity. Unlike methods designed for natural bias, AutoDebias effectively addresses subtle, injected stereotypes and multiple interacting attacks. We evaluate the framework on a new benchmark covering 17 distinct backdoor attack scenarios, including challenging cases where multiple backdoors co-exist. AutoDebias detects malicious patterns with 91.6% accuracy and reduces the backdoor success rate from 90% to negligible levels, while preserving the visual fidelity of the original model.
Stereo video inpainting, which aims to fill the occluded regions of warped videos with visually coherent content while maintaining temporal consistency, remains a challenging open problem. The regions to be filled are scattered along object boundaries and occupy only a small fraction of each frame, leading to two key challenges. First, existing approaches perform poorly on such tasks due to the scarcity of high-quality stereo inpainting datasets, which limits their ability to learn effective inpainting priors. Second, these methods apply equal processing to all regions of the frame, even though most pixels require no modification, resulting in substantial redundant computation. To address these issues, we introduce three interconnected components. We first propose Gradient-Aware Parallax Warping (GAPW), which leverages backward warping and the gradient of the coordinate mapping function to obtain continuous edges and smooth occlusion regions. Then, a Parallax-Based Dual Projection (PBDP) strategy is introduced, which incorporates GAPW to produce geometrically consistent stereo inpainting pairs and accurate occlusion masks without requiring stereo videos. Finally, we present Sparsity-Aware Stereo Inpainting (SASI), which reduces over 70% of redundant tokens, achieving a 10.7xspeedup during diffusion inference and delivering results comparable to its full-computation counterpart, enabling real-time processing of HD (768x1280) videos at 25\,FPS on a single A100 GPU.
Joint estimation of surface normals and depth is essential for holistic 3D scene understanding, yet high-resolution prediction remains difficult due to the trade-off between preserving fine local detail and maintaining global consistency. To address this challenge, we propose the Ultra Resolution Geometry Transformer (URGT), which adapts the Visual Geometry Grounded Transformer (VGGT) into a unified multi-patch transformer for monocular high-resolution depth-normal estimation. A single high-resolution image is partitioned into patches that are augmented with coarse depth and normal priors from pre-trained models, and jointly processed in a single forward pass to predict refined geometric outputs. Global coherence is enforced through cross-patch attention, which enables long-range geometric reasoning and seamless propagation of information across patches within a shared backbone. To further enhance spatial robustness, we introduce a GridMix patch sampling strategy that probabilistically samples grid configurations during training, improving inter-patch consistency and generalization. Our method achieves state-of-the-art results on UnrealStereo4K, jointly improving depth and normal estimation---reducing AbsRel from 0.0582 to 0.0291, RMSE from 2.17 to 1.31, and lowering mean angular error from 23.36deg to 18.27deg--while producing sharper and more stable geometry. The proposed multi-patch framework also demonstrates strong zero-shot and cross-domain generalization and scales effectively to very high resolutions, offering an efficient and extensible solution for high-quality geometry refinement.
Neural Field-Based 3D Surface Reconstruction of Microstructures from Multi-Detector Signals in Scanning Electron Microscopy
PDF ↗The 3D characterization of microstructures is crucial for understanding and designing functional materials. However, the scanning electron microscope (SEM), widely used in scientific research, captures only 2D electron intensity distributions. Existing SEM 3D reconstruction methods struggle with textureless regions, shadowing artifacts, and calibration dependencies, whereas advanced learning-based approaches fail to generalize to microscopic SEM domains due to the lack of physical priors and domain-specific data. We introduce NFH-SEM, a neural field-based hybrid framework that reconstructs high-fidelity 3D surfaces from multi-view, multi-detector SEM images. NFH-SEM integrates coarse multi-view geometry with photometric stereo cues from detector signals through a continuous neural field, incorporating a learnable forward model that embeds SEM imaging physics for self-calibrated, shadow-robust reconstruction. NFH-SEM achieves precise recovery across diverse specimens, revealing 478 nm layered features in two-photon lithography samples, 782 nm surface textures on pollen grains, and 1.559 mm fracture steps on silicon carbide particles, demonstrating its accuracy and broad applicability. Our code and real-world dataset are available at https://github.com/zju3dv/NFH-SEM.
Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consistency, and cross-domain generalization. To address these challenges, we present DriveMVS, a novel multi-view stereo framework that reconciles these competing objectives through two key insights: (1) Sparse but metrically accurate LiDAR observations can serve as geometric prompts to anchor depth estimation in absolute scale, and (2) deep fusion of diverse cues is essential for resolving ambiguities and enhancing robustness, while a spatio-temporal decoder ensures consistency across frames. Built upon these principles, DriveMVS embeds the LiDAR prompt in two ways: as a hard geometric prior that anchors the cost volume, and as soft feature-wise guidance fused by a triple-cue combiner. Regarding temporal consistency, DriveMVS employs a spatio-temporal decoder that jointly leverages geometric cues from the MVS cost volume and temporal context from neighboring frames. Experiments show that DriveMVS achieves state-of-the-art performance on multiple benchmarks, excelling in metric accuracy, temporal stability, and zero-shot cross-domain transfer, demonstrating its practical value for scalable, reliable autonomous driving systems. Code: https://github.com/Akina2001/DriveMVS.git.
DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection
PDF ↗Harmful memes convey offensive intent through implicit associations between visual symbols and text, requiring a broad understanding of cultural stereotypes and visual metaphors. Small-scale Multimodal Large Language Models (MLLMs) often lack the knowledge required to identify such implicit hate, whereas Large-scale MLLMs, despite their broader knowledge, exhibit systematic labeling bias. To address these challenges, we propose DR-HM, a Distill-then-Reinforce training framework with cognition-aware data synthesis for harmful meme detection, which aims to transfer knowledge from closed-source models while mitigating their biases. DR-HM introduces a six-step structured data synthesis scheme with self-refinement that decomposes meme analysis into a progressive, human-inspired reasoning process from entity recognition to harmfulness judgment. Based on the synthesized reasoning data, we further adopt a Distill-then-Reinforce training strategy. This approach combines a two-stage Supervised Fine-Tuning (SFT) with an Adaptive Group Relative Policy Optimization (A-GRPO) algorithm, which incorporates class-ratio-aware reward weighting and dynamic KL coefficients. Experiments on three benchmark datasets show that the proposed approach consistently outperforms existing methods and achieves an accuracy of 84.7% on the FHM dataset, approaching the reported performance of human annotators.
Multimodal Large Language Models (MLLMs) are increasingly deployed as social agents, yet their ability to integrate conflicting identity cues remains underexplored. We audit gender bias in ten recent MLLMs using a counterfactual cooperative gaming task that pairs synthetic voices with avatars of varying gender presentation and visual fidelity. Our analysis reveals distinct bias patterns that can occur independently: closed-source models (e.g., Gemini 2.5/3) exhibit a near-deterministic “voice-matching” bias that enforces binary alignment between voice and appearance, whereas open-weight models (e.g., Qwen-2.5-Omni-7B) show limited responsiveness to vocal cues and instead exhibit context-driven stereotypes, such as preferring male avatars in combat scenarios. We further find that reducing visual realism attenuates matching tendencies in some models. These findings demonstrate that multimodal fairness is not monolithic; models may appear unbiased on one dimension while enforcing strict identity congruence or role-based stereotypes on another. Code and data are available at https://github.com/halfhoon/whose-voice-whose-avatar.
Large Language Models (LLMs) can exhibit imbalanced biases against vulnerable groups, but how they rationalize stereotypes and rights restrictions targeting mental health entities remains underexplored. We audit a broad suite of open-weight LLMs on stereotype-justification prompts tied to mental health identities. We find that several widely used models endorse harmful stereotypes when explicitly asked to justify them, with endorsement varying across model families, versions, and mental health conditions. Finally, we show that widely used harmful-content evaluation and moderation frameworks often miss these nuanced, discriminatory responses, highlighting a gap in current AI safety evaluation for mental health groups.