CVPR 2026 Papers

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.

Feed-forward view synthesis models predict a novel view in a single pass with minimal 3D inductive bias. Existing works encode cameras as Plucker ray maps, which tie predictions to the arbitrary world coordinate gauge and make them sensitive to small camera transformations, thereby undermining geometric consistency. In this paper, we ask what inputs best condition a model for robust and consistent view synthesis. We propose projective conditioning, which replaces raw camera parameters with a target-view projective cue that provides a stable 2D input. This reframes the task from a brittle geometric regression problem in ray space to a well-conditioned target-view image-to-image translation problem. Additionally, we introduce a masked autoencoding pretraining strategy tailored to this cue, enabling the use of large-scale uncalibrated data for pretraining. Our method shows improved fidelity and stronger cross-view consistency compared to ray-conditioned baselines on our view-consistency benchmark. It also achieves state-of-the-art quality on standard novel view synthesis benchmarks.

Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we introduce Sparse to Dense lifting (S2D), a novel pipeline that bridges the two representations and achieves high-quality 3DGS reconstruction with minimal inputs. Specifically, the S2D lifting is two-fold. We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing. Meanwhile, to reconstruct 3D consistent scenes, we also design a corresponding reconstruction strategy with random sample drop and weighted gradient for robust model fitting from sparse input views to dense novel views. Extensive experiments show that S2D achieves the best consistency in generating novel view guidance and first-tier sparse view reconstruction quality under different input sparsity. By reconstructing stable scenes with the least possible captures among existing methods, S2D enables minimal input requirements for 3DGS applications.

We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectively estimates real-world-scale ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras, enabling robust trajectory dataset construction from rare driving events recorded in dashcam.Existing VO methods are trained on fixed observation frequency (e.g., 10Hz or 12Hz), completely overlooking temporal dynamics information. Many prior methods also require calibrated cameras with known intrinsic parameters. Consequently, their performance degrades when (1) deployed under unseen observation frequencies or (2) applied to uncalibrated cameras. These significantly limit their generalizability to many downstream tasks, such as extracting trajectories from dashcam footage.To address these challenges, OpenVO (1) explicitly encodes temporal dynamics information within a two-frame pose regression framework and (2) leverages 3D geometric priors derived from foundation models. We validate our method on three major autonomous-driving benchmarks - KITTI, nuScenes, and Argoverse 2 - achieving more than 20% performance improvement over state-of-the-art approaches. Under varying observation rate settings, our method is significantly more robust, achieving 46%-92% lower errors across all metrics.These results demonstrate the versatility of OpenVO for real-world 3D reconstruction and diverse downstream applications.

Vision Transformers (ViTs) achieve state-of-the-art performance on a wide range of vision tasks, yet they remain highly vulnerable to adversarial perturbations due to the lack of explicit region-level semantic modeling. Adversarial perturbations are typically local and spatially structured, whereas the globally coupled self-attention and spatially uniform feed-forward networks in ViTs propagate local corruptions across the whole image without enforcing consistency within semantically coherent regions. To mitigate this mismatch, we propose Region-aware Mixture-of-Experts, namely "ReMoE", a plug-and-play module that replaces the standard feed-forward network (FFN) with a region-aware expert layer. Specifically, our ReMoE strategically introduces multi-granularity experts (i.e., global, center, and regional) and couples them with an attention-guided routing mechanism that operates on patch-to-region (P2R) and region-to-patch (R2P) transformations. This mechanism adaptively activates the most relevant experts for each spatial location according to its attention profile, enabling the model to capture region-level semantics and local context while preserving global consistency, thereby providing a stronger inductive bias for adversarially robust ViT representations. Extensive experiments demonstrate that our ReMoE substantially improves the adversarial robustness of ViTs with only marginal additional computational cost. Our code is available at https://github.com/zhongskr0114/ReMoE.

3D Gaussian Splatting can exploit frustum culling and level-of-detail strategies to accelerate rendering of scenes containing a large number of primitives. However, the semi-transparent nature of Gaussians prevents the application of another highly effective technique: occlusion culling. We address this limitation by proposing a novel method to learn the viewpoint-dependent visibility function of all Gaussians in a trained model using a small, shared MLP across instances of an asset in a scene. By querying it for Gaussians within the viewing frustum prior to rasterization, our method can discard occluded primitives during rendering. Leveraging Tensor Cores for efficient computation, we integrate these neural queries directly into a novel instanced software rasterizer. Our approach outperforms the current state of the art for composed scenes in terms of VRAM usage and image quality, utilizing a combination of our instanced rasterizer and occlusion culling MLP, and exhibits complementary properties to existing LoD techniques. The source code is available at https://brent-zoomers.github.io/nvgs/

In the deep-learning-based computer vision community, Neural Architecture Search (NAS) has become the de-facto tool for acquiring task-optimal network structures. Nevertheless, NAS methods are trapped in a fundamental accuracy-efficiency dilemma: training-based approaches deliver reliable performance but incur prohibitive search costs, whereas training-free strategies are ultra-fast but often yield relatively unreliable rankings. To reconcile this conflict, we propose a vision-oriented lightweight training-based NAS framework. We first design six micro vision tasks whose training time is negligible, yet together they probe a broad spectrum of representational capacities. Built upon these tasks, we introduce a budget-adaptive performance evaluator to produce the most accurate ranking attainable within the limit. Experiments on popular NAS benchmarks show that our method achieves a ranking correlation higher than existing methods. Furthermore, we construct a search space from prevalent neural blocks and run our method at a cost close to training-free methods; the discovered architecture surpasses the current state-of-the-art under identical training recipes. Our codes are available at https://github.com/fanyi-plus/tf-nas.

Recently, the community has witnessed significant progress in human modeling from single or multi-view inputs. However, these approaches often rely on guessing the occluded regions through either generative models or template fitting. In this work, we address these challenges by exploring optimal fusion strategies using only sparse multi-view inputs. We propose SMVRT, an end-to-end implicit 3D reconstruction framework for sparse multi-view human modeling. Our key contribution lies in the fusion blocks at three stages of the network. First, local and global features alternative fusion modules are designed to enhance 2D features. Second, attentional fusion is performed on warped multi-view and multi-level 2D features to form 3D feature grid. The feature grid aggregates spatially coherent multi-view features by 3D regularization. Third, attentional 2D-3D feature aggregation generates the enhanced latent embeddings for query points to decode occupancies. Experiments on the THUman 2.0/2.1, MultiGarment and MultiHuman datasets demonstrate that our system significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Recent advances in 4D scene reconstruction have greatly improved dynamic modeling across various domains. However, existing approaches remain limited under aerial conditions with single-view capture, wide spatial range, and dynamic objects of limited spatial footprint and large motion disparity. These challenges cause severe depth ambiguity and unstable motion estimation, making monocular aerial reconstruction inherently ill-posed.To this end, we present AeroDGS, a physics-guided 4D Gaussian splatting framework for monocular UAV videos. AeroDGS introduces a Monocular Geometry Lifting module that reconstructs reliable static and dynamic geometry from a single aerial sequence, providing a robust basis for dynamic estimation. To further resolve monocular ambiguity, we propose a Physics-Guided Optimization module that incorporates differentiable ground-support, upright-stability, and trajectory-smoothness priors, transforming ambiguous image cues into physically consistent motion.The framework jointly refines static backgrounds and dynamic entities with stable geometry and coherent temporal evolution. We additionally build a real-world UAV dataset that spans various altitudes and motion conditions to evaluate dynamic aerial reconstruction. Experiments on synthetic and real UAV scenes demonstrate that AeroDGS outperforms state-of-the-art methods, achieving superior reconstruction fidelity in dynamic aerial environments.

Aerial Vision-and-Language Navigation (Aerial VLN) enables unmanned aerial vehicles (UAVs) to follow natural language instructions and navigate complex urban environments.While recent advances have achieved progress through large-scale memory graphs and lookahead path planning, they remain limited by shallow instruction understanding and high computational cost. In particular, existing methods rely primarily on landmark descriptions, overlooking directional cues--a key source of spatial context in human navigation.In this work, we propose LookasideVLN, a new paradigm that exploits directional cues in natural language to achieve both more accurate spatial reasoning and greater computational efficiency. LookasideVLN comprises three core components: (1) an Egocentric Lookaside Graph (ELG) that dynamically encodes instruction-relevant landmarks and their directional relationships, (2) a Spatial Landmark Knowledge Base (SLKB) that provides lightweight memory retrieval from prior navigation experiences, and (3) a Lookaside MLLM Navigation Agent that aligns multimodal information from user instructions, visual observations, and landmark-direction information from ELG for path planning.Extensive experiments show that LookasideVLN significantly outperforms the state-of-the-art CityNavAgent, even with a single-level lookahead, demonstrating that leveraging directional cues is a powerful yet efficient strategy for Aerial VLN.

We present **Vista4D**, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a different camera trajectory and viewpoint. Existing video reshooting methods often struggle with depth estimation artifacts of real-world dynamic videos, while also failing to preserve content appearance and maintain precise camera control for challenging new trajectories. We build a 4D-grounded point cloud representation with static pixel segmentation and 4D reconstruction to explicitly preserve seen content and provide rich camera signals, and we train with reconstructed multiview dynamic data for robustness against point cloud artifacts during real-world inference. Our results demonstrate improved 4D consistency, camera control, and visual quality compared to state-of-the-art baselines under a variety of videos and camera paths. Moreover, our method generalizes to real-world applications such as dynamic scene expansion and 4D scene recomposition. Results are best viewed as videos in the Supplement.

3D Gaussian splatting (3DGS) has become a vital tool for learning a radiance field from multiple posed images. Although 3DGS shows great advantages over NeRF in terms of rendering quality and efficiency, it remains a research challenge to further improve the efficiency of learning 3D Gaussians. To overcome this challenge, we propose novel training strategies and losses to shorten each Gaussian list used to render a pixel, which speeds up the splatting by involving fewer Gaussians along a ray. Specifically, we shrink the size of each Gaussian by resetting their scales regularly, encouraging smaller Gaussians to cover fewer nearby pixels, which shortens the Gaussian lists of pixels. Additionally, we introduce an entropy constraint on the alpha blending procedure to sharpen the weight distribution of Gaussians along each ray, which drives dominant weights larger while making minor weights smaller. As a result, each Gaussian becomes more focused on the pixels where it is dominant, which reduces its impact on nearby pixels, leading to even shorter Gaussian lists. Eventually, we integrate our method into a rendering resolution scheduler which further improves efficiency through progressive resolution increase. We evaluate our method by comparing it with state-of-the-art methods on widely used benchmarks. Our results show significant advantages over others in efficiency without sacrificing rendering quality.

Understanding how the 3D world evolves over time is a fundamental task in computer vision, essential for embodied settings, autonomous driving, etc. It requires not only the reconstruction of the observed scene but also the anticipation of how the scene dynamics will unfold in the future. While the area of 3D reconstruction has progressed rapidly with the advent of recent feed-forward neural networks, forecasting future dynamics in 3D, given the 2D frames of a video remains unexplored. We present Point4Cast, a unified framework that processes streaming 2D frame sequences of a video to estimate the past, present, and future of the underlying dynamic scene, in 3D. At the core of our approach lies a persistently evolving latent spacetime representation that models the environment's evolution across time. Upon receiving a new 2D frame, an update operation integrates the incoming evidence to refine the latent spacetime representation. When queried for any time instant, whether before, at, or beyond the timestamp of the last update. A readout procedure predicts temporally conditioned point maps and camera parameters describing the scene geometry at the queried time. Unlike prior approaches for online dynamic scene reconstruction that estimate each frame's point map solely at the timestamp of the last observed frame, Point4Cast achieves coherent reconstruction across any queried time. Empirical evaluations show that Point4Cast achieves state-of-the-art performance on streaming dynamic scene reconstruction and forecasting benchmarks, across multiple challenging datasets, while providing scene flow estimation and forecasting for free. The code will be released publicly.

Vision Transformers (ViTs) have recently achieved state-of-the-art performance in 2D human pose estimation due to their strong global modeling capability. However, existing ViT-based pose estimators are designed for static images and process each frame independently, thereby ignoring the temporal coherence that exists in video sequences. This limitation often results in unstable predictions, especially in challenging scenes involving motion blur, occlusion, or defocus. In this paper, we propose TAR-ViTPose, a novel Temporal Aggregate-and-Restore Vision Transformer tailored for video-based 2D human pose estimation. TAR-ViTPose enhances static ViT representations by aggregating temporal cues across frames in a plug-and-play manner, leading to more robust and accurate pose estimation. To effectively aggregate joint-specific features that are temporally aligned across frames, we introduce a joint-centric temporal aggregation (JTA) that assigns each joint a learnable query token to selectively attend to its corresponding regions from neighboring frames. Furthermore, we develop a global restoring attention (GRA) to restore the aggregated temporal features back into the token sequence of the current frame, enriching its pose representation while fully preserving global context for precise keypoint localization. Extensive experiments demonstrate that TAR-ViTPose substantially improves upon the single-frame baseline ViTPose, achieving a +2.3 mAP gain on the PoseTrack2017 benchmark. Moreover, our approach outperforms existing state-of-the-art video-based methods, while also achieving a noticeably higher runtime frame rate in real-world applications. Project page: https://github.com/zgspose/TARViTPose.

Reproducible closed-loop evaluation remains a major bottleneck in Embodied AI such as visual navigation. A promising path forward is high-fidelity simulation that combines photorealistic sensor rendering with geometrically grounded interaction in complex, open-world urban environments. Although recent video-3DGS methods ease open-world scene capturing, they are still unsuitable for benchmarking due to large visual and geometric sim-to-real gaps. To address these challenges, we introduce Wanderland, a real-to-sim framework that features multi-sensor capture, reliable reconstruction, accurate geometry, and robust view synthesis. Using this pipeline, we curate a diverse dataset of indoor-outdoor urban scenes and systematically demonstrate how image-only pipelines scale poorly, how geometry quality impacts novel view synthesis, and how all of these adversely affect navigation policy learning and evaluation reliability. Beyond serving as a trusted testbed for embodied navigation, Wanderland's rich raw sensor data further allows benchmarking of 3D reconstruction and novel view synthesis models. Our work establishes a new foundation for reproducible research in open-world embodied AI.

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the image or representation space. This appearance-centric view overlooks body contacts and couplings, conflates function-critical regions (e.g., aerodynamic or hydrodynamic surfaces) with ornamentation, and reconstructs structures suboptimally, even when physical regularizers are added. All these can lead to unphysical and implausible interactions. To address this, we consider the question: How can 3D reconstruction become aware of real-world interactions and underlying object functionality, beyond visual cues? To answer this question, we propose FluidGaussian, a plug-and-play method that tightly couples geometry reconstruction with ubiquitous fluid-structure interactions to assess surface quality at high granularity. We define a simulation-based uncertainty metric induced by fluid simulations and integrate it with active learning to prioritize views that improve both visual and physical fidelity. In an empirical evaluation on NeRF Synthetic (Blender), Mip-NeRF 360, and DrivAerNet++, our FluidGaussian method yields up to +8.6% visual PSNR (Peak Signal-to-Noise Ratio) and -62.3% velocity divergence during fluid simulations. Our code is available in https://github.com/delta-lab-ai/FluidGaussian.

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a geometry-driven video world model that generates dynamic, realistic videos from a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state as a static background point cloud and per-object 3D Gaussian trajectories. This representation captures each object's motion path and probabilistic 3D occupancy over time, providing a flexible, category-agnostic alternative to rigid bounding boxes and parametric models. We render 4D Geometric Control into 4D control maps for a pretrained video diffusion model, enabling high-fidelity, view-consistent video generation that faithfully follows the specified dynamics. To enable training at scale, we develop an automatic data engine and construct VerseControl4D, a real-world dataset of 35K training samples with automatically derived prompts and rendered 4D control maps. Extensive experiments show that VerseCrafter achieves superior visual quality and more accurate control over camera and multi-object motion than prior methods.

Point cloud anomaly detection is crucial in automated manufacturing, with reconstruction-based diffusion methods emerging as a mainstream solution. However, these approaches still face two major challenges: (1) geometry violation, where random noise perturbations deviate from local surface normals, causing structural distortion; and (2) undistinguished reference regions, where uniformly applied coarse anomaly embeddings during denoising blur normal details and impede accurate anomaly recovery. To address these issues, we propose AARD, a geometry-aligned and anomaly-aware diffusion reconstruction framework. We argue that high-fidelity anomaly detection requires a principled reformulation of the diffusion process: noise should align with geometry to preserve structures, and reconstruction can be better guided by anomaly-aware references to discriminatively recover normal details while correcting defects. AARD progressively aligns noise directions with vertex normals while maintaining vertex-graph consistency, and employs an adaptive transformer that assigns normal references to anomalous regions and input references to normal areas. Experiments on Anomaly-ShapeNet and Real3D-AD show that AARD consistently outperforms state-of-the-art approaches, achieving superior geometric fidelity and robust anomaly localization.

We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative image-based light decomposition model that factorizes complex indoor scene illumination into its constituent light sources. This factorization enables independent manipulation of individual light sources, specifically allowing control over their state (on/off), chromaticity, and intensity. We further introduce multi-view lighting harmonization to ensure consistent propagation of the lighting decomposition across all scene views. This is integrated into a relightable 3D Gaussian splatting representation, providing real-time interactive control over the individual light sources. Our results demonstrate highly photorealistic lighting decomposition and relighting outcomes across diverse indoor scenes. We evaluate our method on both synthetic and real-world datasets and provide a quantitative and qualitative comparison to state-of-the-art techniques.