NeurIPS 2026 Papers

Streaming 3D reconstruction enables long-sequence scene understanding by processing frames sequentially, but remains challenging when historical information is compressed into a fixed-size recurrent state. Existing training-free extensions of implicit-memory models mainly improve stability by modulating update magnitude, thereby controlling how strongly each incoming observation modifies the state. However, the geometric intent of the update, namely the direction in which the observation drives the recurrent state, remains tied to the current-frame candidate and can accumulate directional bias over long sequences. We propose HIST3R, a training-free framework that improves recurrent state updating through a direction--magnitude decomposition. HIST3R uses historical recurrent states to rectify the update direction, providing a more reliable geometric intent, and uses observation--history visual discrepancy to modulate the update magnitude, yielding an adaptive update gain. This design makes recurrent updates less dependent on isolated frame-level evidence while preserving the bounded-memory advantage of implicit streaming reconstruction. Across camera pose estimation, 3D reconstruction, and video depth estimation benchmarks, HIST3R consistently improves long-horizon performance without additional training. At 1000 input frames, HIST3R achieves relative ATE reductions of 23.87% on ScanNet and 33.65% on TUM-Dynamics over the strongest baseline; at 500 frames, it improves long-sequence 3D reconstruction over the strongest CUT3R-based variant by 56.52% on 7-Scenes and 33.96% on NRGBD.

Visual geometry grounded transformers are strong feed-forward backbones for multi-view 3D reconstruction, but their global attention becomes the dominant inference bottleneck as views and tokens increase. Existing acceleration methods mainly focus on designing compression operators, while compression strengths are often assigned by uniform schedules or offline calibration, overlooking the heterogeneous sensitivity of layers and attention heads during inference. We therefore propose HALO-VGGT, a lightweight online compression allocator requiring no training or calibration for efficient VGGT-style reconstruction. HALO uses a sampled query-key margin proxy to estimate compression sensitivity at the layer and head levels, and assigns hierarchical compression ratios accordingly. Instead of introducing a new compression operator, HALO complements pruning, merging, and sparse attention mechanisms by deciding where and how aggressively they should be applied. HALO improves the trade-off between speedup and accuracy across datasets and backbones, achieving up to 4.20$\times$ speedup and further improving existing merging and sparsity methods by up to 1.46$\times$.

Generating egocentric video from a single exocentric video is an emerging and important topic for AR/VR and physical AI. Compared with conventional novel view synthesis, exo-to-ego generation is a significantly harder task due to the extreme viewpoint changes, extensive occlusions and unobserved regions, where standard geometric conditioning becomes highly unreliable. We present Grounded-Exo2Ego, a principled framework that addresses these challenges at both the architectural and data levels. Architecturally, Grounded-Exo2Ego is a dual-branch video diffusion model that couples a geometric anchoring branch, which renders a 3D reconstruction into the ego view for overall scene layout, with a semantic grounding branch, which anchors per-object context onto the scene geometry so that heavily distorted regions and unobserved regions can be hallucinated in context. Additionally, to support robust training at scale, we substantially improve the labeling pipeline for real-world data and further develop a fully automated synthetic data engine that renders high-quality dynamic humans in procedurally generated environments. Evaluations on the challenging EgoExo4D dataset show that our method substantially outperforms recent state-of-the-art approaches. Extensive ablations further demonstrate that both structured semantic grounding and rigorous data processing are essential for robust exo-to-ego video generation in extreme scenarios.

Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed-forward manner. However, their computational cost grows quadratically with the input sequence length due to the global attention layers inside these models. This limits both their scalability and efficiency. In this work, we address this challenge with a simple yet general strategy: restricting the number of key/value tokens that each query interacts with during global attention. To achieve effective token selection, we introduce a two-stage framework. First, an inter-frame selection step operates at the frame level to identify frames that should be preserved. Second, an intra-frame selection step further discards more redundant tokens within the selected frames. Our analysis highlights the advantage of a diversity-based strategy for inter-frame selection, which ensures broad coverage of the scene. For intra-frame selection, we show that layer-aware sparsification is necessary, with the selection process guided by the entropy of the global attention pattern. Our approach offers a superior speed-accuracy trade-off compared to existing solutions. Extensive experiments show that it accelerates visual geometry transformers by over 85% for scenes with 500 images while maintaining, or even improving, baseline performance, which hints that how our token selection strategy can play a crucial role in future applications of visual geometry transformers.

Recent 3D geometric foundation models, such as VGGT, provide robust feed-forward 3D reconstruction by directly predicting camera poses and 3D scene points from input images. However, their results remain inaccurate, and scaling them to long sequences or large unordered image sets typically requires chunk-wise processing, which can introduce drift and inconsistency. We present Glob3R, a global SfM-style reconstruction built on 3D foundation models. Our key idea is to explicitly optimize feed-forward geometric predictions. To this end, we augment a frozen Pi3X backbone with a lightweight dense matching head that predicts image warps between selected reference frames and neighboring views. These dense warps are converted into sparse but reliable multi-view feature tracks, which provide correspondence constraints for global optimization. We further introduce a keyframe-based sliding-window association strategy that propagates tracks and relative poses across overlapping windows, enabling scalable reconstruction. Finally, we perform global motion averaging and bundle adjustment to refine camera poses, reduce scale inconsistencies, and recover dense scene geometry. Extensive experiments on indoor, outdoor, large-scale driving, and unordered SfM benchmarks demonstrate that Glob3R achieves robust and accurate reconstruction. It consistently improves over feed-forward foundation-model baselines and recent scalable reconstruction methods, while being more robust than classical SfM pipelines. The refined poses also lead to higher-quality neural rendering, validating the benefit of combining foundation-model priors with global geometric optimization.

Georeferenced 3D reconstruction from ground images requires recovering not only the local scene geometry, but also localizing every camera center and 3D point in a geographic coordinate frame. Existing cross-view geo-localization methods are able to estimate the GPS coordinate of a single ground camera, but do not reconstruct dense 3D geometry, while recent feed-forward multi-view 3D reconstruction models predict point maps and camera poses only in a local coordinate frame. We introduce X-VGGT, a feed-forward cross-view geometry transformer that unifies these tasks by jointly processing a set of ground-level images and a georeferenced satellite image to predict ground-view camera poses, dense 3D point maps, and a scene-level transform parameterized by gravity, scale, yaw and translation. This transform projects the local reconstruction into the satellite plane, resulting in the geo-registration of all ground images with the georeferenced satellite image. We further propose an evaluation protocol for this joint task and show that, across multiple cross-view datasets, X-VGGT produces accurate georeferenced reconstructions and outperforms prior cross-view geo-localization and multi-view 3D reconstruction models.

Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated under ideal, degradation-free imaging conditions, whereas real-world observations often contain various degradations that differ significantly from such settings. Improving robustness for multi-view 3D reconstruction under degraded conditions therefore remains an important challenge. We present Geometry-Aware Representation Denoising (GARD), a novel framework that performs diffusion-based multi-view restoration directly in the feature space of a feed-forward reconstructor. This design exploits the geometry-aware feature representations of the reconstructor to effectively recover accurate scene geometry. Furthermore, by employing a decoder, the refined representations can also be used to restore high-quality RGB images, thereby enabling the simultaneous recovery of 3D scene geometry and high-quality imagery. Comprehensive experiments on the DA3 benchmark demonstrate the effectiveness of the proposed GARD framework. Our code and weights will be publicly released for full reproducibility.

Panoramic 3D reconstruction enables the holistic recovery of complete 360° scene geometry, offering significant benefits for robotics and AR/VR applications; however, direct estimation is persistently challenged by inherent spherical distortions. While decomposing panoramas into perspective views effectively circumvents these distortions and leverages established geometric priors, this strategy often fails to preserve global spatial consistency across the decomposed views. To address this fundamental limitation, we introduce an enhanced feed-forward 3D reconstruction framework tailored for the view decomposition paradigm, driven by two key innovations. First, we propose a geometry-aware attention aggregator, featuring a streamlined attention architecture equipped with a novel Geo-Attention mechanism, which explicitly exploits the intrinsic global information to guide multi-view interactions, thereby suppressing geometrically inconsistent matches. Furthermore, to jointly enforce fine-grained local details and global structural coherence, we formulate a hybrid supervision strategy. Specifically, we complement the standard local point cloud loss in perspective space with our newly proposed spatially uniform global point cloud loss in panoramic space, explicitly enforcing holistic constraints within a unified coordinate system. Experiments demonstrate that our method consistently outperforms prior single-view panoramic approaches in reconstruction accuracy, geometric consistency, and visual fidelity.

Flow matching has recently emerged as a flexible and efficient framework for generative modelling by learning deterministic transport dynamics between probability measures. In this work, we extend flow matching to the space of probability measures over probability measures, introducing a Wasserstein-on-Wasserstein (WoW) formulation. Leveraging the nested Wasserstein geometry, we show that measures over transport plans naturally induce velocity fields that realize metameasure flows. This yields a principled generalization of Wasserstein flow matching via coupled outer and inner transport plans. To address the substantial computational cost of WoW transport, we propose scalable approximations based on sliced and linear Wasserstein distances, enabling efficient training while promoting numerically stable, near-straight trajectories. Our framework unifies and extends existing approaches to point cloud and set generation, providing a practical and theoretically grounded method for generative modelling in WoW spaces.

We introduce a new approach to high-fidelity 3D scene reconstruction from multi-view RGB images that tightly couples reconstruction with a strong generative 3D prior. We cast scene reconstruction as conditional 3D generation over a set of spatially-localized, overlapping chunks that together tile the scene, scaling generation to large scene extents. Crucially, we inherit the fidelity and completeness of state-of-the-art generative shape models -- we use Trellis.2 as an example -- which we generalize to the scene level. To this end, we propose a projection-based conditioning mechanism that lifts posed multi-view image features into a coherent 3D representation aligned with the generative model, independent of view ordering and spatially anchored to the scene, yielding high-fidelity, multi-view consistent generated geometry. This enables lifting the strong object-level prior of Trellis.2 to multi-view, scene-scale generation, producing faithful, editable PBR mesh reconstructions of indoor environments. As a result, we obtain high-fidelity results that outperform cutting-edge reconstruction methods by 16\%.

Generative novel view synthesis from sparse input images is rarely all reconstruction or all generation: pixels visible in some source view have a unique correct value modulated only by view-dependent shading, while pixels in disocclusions or beyond the captured volume admit a distribution of plausible completions. Existing generative novel-view-synthesis methods conflate these regimes under a single uniform loss, blurring the line between geometric fidelity and creative hallucinations even when scene geometry is injected through warped point clouds or projected depth. We introduce GenRec, a multi-view flow matching model that builds the reconstruction--generation split directly into its architecture, supervision, and gradient flow. Guided by an observation mask derived from the source cameras and a monocular depth estimator, a flow matching backbone jointly denoises RGB and scene-coordinate maps across all target views, while a pixel-space refinement stage restores high-frequency detail on observed pixels; the same mask gates supervision so regression signals do not contaminate the generative prior. Across RealEstate10K, DL3DV-10K, and Mip-NeRF~360, in both single-view extrapolation and two-view interpolation, GenRec attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved ones, showing the effectiveness of our approach. The source code and trained models will be made public.

Immersive video communication requires photorealistic, render-efficient, and compact dynamic scene representations. 3D Gaussian Splatting (3DGS) offers a promising representation, but dynamic 3DGS remains difficult to compress due to dense primitives and spatiotemporal redundancy. Anchor-based formulations improve compactness with sparse scaffolds that share geometry and appearance across primitives. However, existing designs often rely on deforming a single canonical scaffold and condition each primitive on its associated anchor in isolation, limiting their ability to handle non-local dynamics and disocclusion while under-exploiting inter-anchor correlations, particularly in motion- or texture-dense regions. To address these limitations, we propose SAGA, a volumetric video codec built upon Sparse Anchor-assisted GAussian splatting representations. SAGA represents dynamic 3D scenes using hierarchically organized sparse 4D anchors, where coordinate-based INR decoders generate fine anchors and Gaussian primitives from inter-anchor interpolations, enabling compact parameter sharing across spatiotemporal structures. For long-range dependencies among unstructured anchors, we further introduce fixed-size memory slots with orthogonality-informed updates for accurate entropy-context modeling. Experiments show SAGA outperforms the state-of-the-art 3DGS-based codec GIFStream, achieving BD-rate reductions of 84.54% on Neu3D, 76.71% on Panoptic Sports, and 83.94% on MPEG MIV.

This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-based density maps and are sensitive to spatial resolution, GDSNet represents a crowd as a superposition of continuous 2D Gaussian primitives. Our approach is built upon two key contributions. First, we introduce a control-point-based fitting mechanism to structure the prediction of the Gaussian parameters. We design a method to allocate a set of control points that define local regions, from which features are pooled to regress each primitive's parameters. Second, we adapt a differentiable Gaussian Splatting framework to the counting task by parameterizing each primitive with geometric parameters and a scalar density mass. This formulation allows the network to be trained end-to-end via spatial matching of differentiably rendered density maps, naturally providing both local density supervision and global count optimization. Extensive evaluations on four standard benchmarks show GDSNet consistently outperforms the state of the art. Code will be released upon acceptance.

Streaming 3D reconstruction from long monocular video sequences requires maintaining a key-value(KV) cache that grows linearly with sequence length, creating a severe memory bottleneck. Existing approaches either truncate the cache to a fixed set of anchor frames, leading to reconstruction quality degradation, or rely on attention-score heuristics that are agnostic to 3D scene structure,failing to preserve geometrically valuable tokens. To address these problems, we present GHOST (Geometry-Hierarchical Online Streaming Token Eviction), a training-free KV cache management framework that exploits the model's own 3D geometry outputs to evict redundant tokens online. GHOST introduces three mutually reinforcing innovations: a hierarchical dual-level importance scoring scheme, a privilege mechanism that protects special tokens from eviction, and a cosine-similarity-guided layer-wise budget allocation. Experiments on various benchmarks show that GHOST preserves excellent reconstruction quality while cutting the KV cache by nearly half and delivering $1.75\times$ faster inference compared to state-of-the-art methods. Our code and model will be available to the public.

Feed-forward 4D Gaussian Splatting (4DGS) offers an efficient paradigm for reconstructing dynamic scenes from monocular videos, bypassing the need for lengthy per-scene optimization. However, existing methods suffer from three fundamental bottlenecks: redundant frame-wise grid-aligned Gaussian representations, suboptimal single-step 2D-to-4D regression architectures, and restrictive dependencies on 3D/4D signals during training or inference. In response to these limitations, we introduce a novel Gaussian-Grounded Query Refinement (GGQR) framework. To reduce representation redundancy, GGQR learns a compact set of Gaussian queries equipped with a newly designed plateau-shaped temporal kernel, enabling single primitives to persistently model motion-consistent regions across extended frames. To alleviate regression ambiguity, these queries are iteratively refined through a Gaussian-grounded attention mechanism operating in 3D space. Specifically, GGQR leverages foundation models to lift 2D observations into a shared 3D space, explicitly grounds the 3D attention in intermediate Gaussian states, and residually updates those Gaussian states. Trained entirely self-supervised on casual videos, GGQR achieves a new state-of-the-art on dynamic reconstruction benchmarks among feed-forward 4DGS methods, delivering superior performance in novel view-time rendering alongside downstream motion modeling tasks.

We present GAMMA, a scalable feed-forward model that reconstructs 4D Gaussian Splatting from monocular videos, enabling dynamic novel view synthesis in real-time. Most existing approaches for dynamic scene modeling require multi-view videos as input or costly per-scene optimization. In contrast, GAMMA learns a unified 4D scene representation from monocular videos and generatively predicts 4D Gaussian primitives within seconds. The key insight of GAMMA lies in its unified Gaussian reconstruction model, which jointly estimates temporally consistent geometry, appearance, and motion from the input video. We further explore its scalability through large-scale training on a comprehensive dataset encompassing both synthetic and real-world videos. The reconstructed GAMMA representation supports interactive scene exploration via real-time rendering across timesteps and local viewpoints. Extensive experiments demonstrate that GAMMA outperforms existing methods in both reconstruction fidelity and efficiency.

Persistence diagrams are common representations in topological data analysis, yet they lack both the vector space structure required by machine learning algorithms and a principled framework for statistical comparison. We introduce STRAND (Survival Topological Representation ANalysis of Diagrams), which treats (collections of) PDs as survival data: each topological feature with persistence value $p = d - b$ is a fully observed time-to-event, and the persistence survival function $S(t) = \mathbb{P}(p > t)$ is the central object for comparing diagrams. From this single representation we derive (i) a non-parametric two-sample test with calibrated Type~I error and high power from a small number of diagrams; (ii) interpretable effect sizes; and (iii) a 1-Wasserstein-stable feature vector for downstream machine learning. We validate calibration and power on synthetic manifolds with controlled topology, demonstrate competitive vectorisation across 14 graph and 3D point cloud benchmarks, and apply the method to study functional brain connectivity in fMRI/neuroscience data. To our knowledge, STRAND is the first method to provide hypothesis testing and vectorisation for persistence diagrams from a single coherent and interpretable representation.

Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception, but their KV-cache grows unbounded over long streams, limiting practical deployment. We study bounded-memory streaming geometry from the perspective of memory organization: unlike language modeling, where useful information can often be compressed at token level, geometry-driven inference relies on coherent and mutually compatible observations across views. Under fixed memory budgets, retaining history as isolated entries can progressively fragment the geometric context needed for stable long-horizon matching and fusion. We therefore propose \textbf{FrameVGGT}, a bounded-memory framework that maintains a fixed-capacity set of complementary memory units for streaming geometry. In our implementation, each unit is instantiated as a frame-wise KV segment summarized by a compact key-space prototype, together with a sparse anchor tier for persistent long-range references. Across long-sequence 3D reconstruction, video depth estimation, and camera pose estimation, FrameVGGT achieves favorable accuracy--memory trade-offs under bounded budgets while maintaining more stable geometry over long streams.

Accurate 3D surface reconstruction from unposed images remains a fundamental challenge in computer vision. Recent feed-forward pointmap methods eliminate the need for camera poses but inherently produce discrete, sparse representations. In this paper, we present a generalizable pipeline that directly recovers continuous, high-fidelity 3D surfaces from unposed images by seamlessly integrating pointmap- and ray-based representations. Our approach first extracts explicit geometric priors using a pre-trained pointmap model. A query-view-centric selection module then identifies geometrically relevant reference views via a robust surfel voting mechanism. Next, a cross-attention regressor bridges target query rays with reference features to estimate a continuous coarse surface. Finally, a raylet-based module aggregates local 3D features to carve out high-frequency details and analytical normals. Trained as a single foundation model on 9 diverse datasets, our method demonstrates exceptional zero-shot generalization on 6 unseen datasets, clearly surpassing state-of-the-art baselines in geometric accuracy and detail preservation.