NeurIPS 2026 论文列表

Rotation averaging on the circle, equivalently robust angular synchronization, underlies a wide range of geometric estimation problems, for example, gravity-aligned Structure-from-Motion, multi-way point-cloud registration, and in-plane alignment in single-particle cryo-electron microscopy. We present the first algorithm that certifiably solves the robust maximum-consensus formulation of this problem to global optimality. Our approach discretises the circle into uniform bins, formulates a pairwise Markov random field and solves it via branch-and-bound with two key novelties: an ICM-guided label-pruning rule that collapses the branching factor to a handful of candidates, and a node-constrained relaxation bound orders of magnitude tighter than the standard per-edge bound. Together, they reduce the search from millions of nodes to a few hundred on typical instances. On all tested problems, the algorithm terminates with a proof of global optimality, typically in a few seconds. Across synthetic graphs and three real-world domains -- image-based SfM on IMC 2023/2024, multi-way LiDAR point-cloud registration on KITTI and NSS, and cryo-EM angular synchronization on EMPIAR-10166 -- the certified solution achieves sub-degree accuracy at outlier rates where other baselines fail. Our code will be made publicly available.

Label-free proxy scores are often used as if they measured downstream discriminative difficulty. A score may rank samples similarly to a target, predict that target on held-out data, or improve a downstream decision; these are distinct evidence levels. We present Cross-Objective Hardness Evaluation (COHE), a protocol that audits each proxy--target--protocol tuple through dependence, held-out-prediction, and operational-transfer gates. COHE maps standard, inspectable statistics to bounded claim tiers; the contribution is evidence-to-claim calibration, not a new sampler, metric, or model. A DINOv2 isolation stress test illustrates the boundary: representation isolation captures a first-learning dynamics relation (rho = 0.4363, R^2 = 0.2126), but using it for direct 10% subset selection fails against Random (Delta = -15.41, 95% CI [-15.87, -14.95]). COHE therefore preserves this target-specific relation without rebranding it as actionability. Across CIFAR-10/100, ImageNet-10, and ImageNet-1K validation checks, eight primary generative proxy families do not support CE/margin surrogate claims: they show weak alignment, near-zero held-out CE prediction, and unreliable transfer. Controls show stable discriminative target rankings, expected shuffled-proxy null behavior, and a task-aligned ImageNet target-hard retrieval/triage tuple that passes all gates. The anonymized supplementary package provides lightweight gate-wise audit code, toy scalar CSV examples, cached aggregate summaries, selected derived scalar arrays for tabular gate verification, supported paper-style verification scripts, and provenance notes without redistributing raw datasets, images, weights, checkpoints, or full scoring/retraining pipelines.

Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. The code will be made public.

Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution “optical biopsies” of human skin {\em in vivo} by acquiring en-face images at successive depths, forming a sparse $z$-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution ($0.5\mu m$) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning ($3\mu m$), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach CD-RCM: a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to $200 \mu m$. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM’s depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.

Training a single 3D Gaussian Splatting (3DGS) scene routinely requires tens of gigabytes of GPU memory, placing standard 3DGS training beyond many low-memory GPU budgets commonly found on laptops and edge devices. Existing low-memory approaches mainly follow two directions: block-wise training and frustum-culling-based host offloading. Block-wise methods train spatial blocks independently and merge them afterward. However, because 3DGS renders each image through depth-ordered alpha compositing over all visible Gaussians, independent block training reduces optimization to block-local objectives, thereby losing full-image loss supervision. Frustum-culling-based host offloading retains full-image loss supervision by jointly rendering all visible Gaussians while uploading only the view-visible subset to the GPU. However, its GPU memory consumption still scales with the size of the visible set, which can exceed the memory budget of low-end GPUs for dense scenes or wide-coverage views. We propose CC-GS, a low-memory block-wise 3DGS training framework based on CPU-GPU block-wise context compositing, featuring three key designs. $\textbf{i) Block-wise context compositing}$: CC-GS partitions Gaussians into capacity-bounded blocks and represents non-optimized visible blocks using cached per-pixel color and residual transmittance, with depth used only for ordering, enabling full-image loss supervision while processing one capacity-bounded block at a time. $\textbf{ii) Two-pass block-wise training}$: each training view is processed through a lightweight context pass for caching compositing context and a differentiable block-optimization pass for updating individual blocks. $\textbf{iii) Asynchronous CPU-GPU execution}$: CC-GS overlaps block gathering, host-device transfers, rendering, backpropagation, gradient offloading, and CPU-side updates through an asynchronous CPU-GPU pipeline. Experiments on standard 3DGS benchmarks show that CC-GS keeps the measured peak GPU memory below 2GB for all evaluated scenes while matching the rendering quality of standard 3DGS. Compared with standard 3DGS, CC-GS, incurs at most a $1.54\times$ dataset-level training slowdown.

3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorithm development is limited as no current datasets provide both a realistic in vivo appearance and dense, time-resolved 3D ground truth, especially under non-rigid deformation. We present C3VD-DEFCOL, a framework and dataset for evaluating deformable colonoscopy reconstruction with paired geometry and realistic texture. Starting from C3VD/C3VDv2 colon meshes and camera trajectories, we generate controlled deformations of the colon surface, including peristaltic waves and centerline motion, and render fisheye colonoscopy videos with frame-level depth, surface normals, optical flow, occlusion masks, camera poses, and time-stamped 3D meshes. We then use the rendered geometry, primarily depth, to condition an LTX-2.3-based sim-to-real translation model that produces RGB clips with in vivo-like mucosal color, texture, vasculature, and specular appearance while preserving the underlying 3D scene structure. The resulting dataset contains 110 videos from 12 unique colon mesh geometries, with varying camera trajectories, appearances, and multiple deformation regimes, including three peristaltic severity levels. We evaluate the generated videos using appearance realism, geometric consistency, and temporal consistency metrics, and use the paired ground truth to benchmark the downstream task of pose estimation in deformable 3D reconstruction. Our experiments show how pose estimation error increases with increasing deformation severity, providing a controlled stress test that is not possible with existing in vivo datasets. Overall, C3VD-DEFCOL is designed as a reproducible, quantitative evaluation platform for testing deformable 3D reconstruction algorithms, with the goal of reducing the domain gap between synthetic datasets and in vivo colonoscopy.

The world appears spatially three-dimensional, however, existing large-scale multimodal models are often built mostly around 1D, 2D and 2.5D modalities. To move toward a more complete and actionable understanding of physical reality, models that natively model and generate across 1D, 2D, and 3D modalities are desirable. We present OmniDiMM, an any-to-any foundation model trained on a diverse set of 3D including meshes, Gaussian splats, voxels, and NeRFs; 2D such as images, and 1D like text. We develop tokenizers for a diverse set of 3D modalities, such as NeRF weights triplanes, UDFs, and 3DGSs, that convert them into discrete tokens, which enables multimodal masked modeling for joint training. Out of the box, OmniDiMM can directly perform standard tasks such as novel-view image synthesis and 3D generation from images, where it matches or outperforms existing specialized methods. As an any-to-any model, it can also convert between different 3D representations, such as NeRF weights and UDF, and generate various 3D modalities from 2D inputs, including DINOv2 features and surface normals. Joint training across 3D modalities also leads to strong transfer performance on downstream tasks such as grasping, classification, and object pose prediction. We scale training to a 1B-parameter model using 1T tokens across 1M objects. The pretrained models, training code, and multimodal dataset will be open-sourced.

Multi-View Stereo (MVS) and Monocular Depth Estimation (MDE) provide complementary cues for dense 3D reconstruction. MDE offers rich contextual and structural priors but lacks explicit geometric constraints, whereas MVS exploits multi-view geometry but often struggles in ambiguous regions, such as weakly textured or reflective surfaces. Existing MDE-assisted MVS methods mainly use monocular cues in a unidirectional manner, leaving the interaction between monocular representations and multi-view cost volumes insufficiently explored. To address this limitation, we introduce BridgeMVS, a unified cascaded MVS framework that bridges monocular depth features and multi-view cost-volume representations through Bidirectional Dynamic Fusion (BDF). At each cascade stage, BDF performs dynamic mono-to-volume and volume-to-mono updates: monocular structural features are injected into cost-volume regularization, while multi-view geometric evidence recalibrates intermediate monocular representations. The enhanced monocular features are propagated across stages together with MVS representations and supervised by stage-wise monocular losses, establishing an auxiliary structural guidance path for the MVS branch. Unlike stage-isolated designs, BridgeMVS preserves fused representations from coarse to fine stages, allowing low-resolution cross-branch interactions to guide subsequent high-resolution depth estimation. Moreover, BDF is designed as a plug-and-play module and can be conveniently integrated into existing cascaded MVS frameworks. Extensive experiments show that BridgeMVS achieves state-of-the-art performance on the DTU, Tanks and Temples, and ETH3D benchmarks.

Differentiable signal parameterizations such as implicit neural representations (INRs) and hybrid models are increasingly central to computational imaging, yet principled tools for evaluating reconstruction fidelity at finite model size remain limited when ground truth is unavailable. We introduce a framework for predicting the reconstruction error of compressive signal parameterizations, yielding non-asymptotic, signal-specific bounds that are both theoretically sound and efficiently computable without access to the ground truth signal. Specifically, we prove that when parameterization-based compression satisfies certain natural properties, the compression error at any compression level is bounded by a simple scaled difference between model predictions at different compression levels. We verify these properties for representative model families including interpolated grids, Fourier feature networks, multi-resolution hash tables, and tensor factorizations, and show empirically that the resulting worst-case guarantees can be efficiently adapted into signal-specific error predictors that are tight and generalizable. Across direct fitting of synthetic and natural signals, and inverse problems including radiance field and MRI reconstruction, our method closely tracks global error curves and yields informative local error heatmaps without ground-truth access.

Novel View Synthesis(NVS) for thermal infrared scenarios has become increasingly important in practical applications. Recent advances in 3D Gaussian Splatting(3DGS) have demonstrated its strong performance for NVS, and a handful of preliminary efforts have explored its extension to thermal NVS. However, existing 3DGS-based methods for thermal NVS often suffer from severe local blur in rendered images. Theoretical and empirical analyses reveal that the single-channel nature of thermal data allows intensity discrepancy to be compensated through opacity adjustment, which can erroneously amplify the opacity of floating Gaussians, especially when the background behind them is insufficiently represented. To address this issue, we propose a method termed Floating Gaussian Suppression (FGS) for thermal NVS. Specifically, we leverage an over-saturation of luminance to introduce an implicit gradient penalty, which guides the optimizer to suppress the opacity of floaters to match the ground truth. By effectively reducing the opacity of these floaters, this mechanism alleviates the occlusion of distant details, thereby mitigating local blur. Moreover, to preserve rendering fidelity, the luminance parameters of Gaussians are optimized concurrently. Experimental results demonstrate that our method effectively mitigates local blur and produces sharper and more faithful thermal reconstruction compared with the baseline methods.

Multi-view video recordings are increasingly used to capture the 3D movements of animals in experimental settings, yet extracting rich 3D representations from these recordings remains challenging. Supervised pose estimation requires extensive manual annotation, while general-purpose 3D reconstruction models trained on generic scene datasets fail on the specialized imagery and sparse-view setting of laboratory experiments. We address these limitations with BEAST3D, a self-supervised pretraining framework that learns 3D visual representations from unlabeled, calibrated multi-view video. BEAST3D uses a vision transformer to predict 3D Gaussian splats that reconstruct held-out views through differentiable rendering, while simultaneously segmenting the animal from the background. BEAST3D reconstructs 3D structure with as few as four views by conditioning directly on known camera parameters---unlike general-purpose models, which must estimate camera geometry from dense overlapping viewpoints that are seldom available in lab settings. Through comprehensive evaluation across four species, we demonstrate that BEAST3D produces rich, viewpoint-invariant features that transfer effectively to three downstream tasks: novel view synthesis, which validates the quality of the learned 3D representations; multi-view pose estimation, which provides the sparse keypoint trajectories widely used in behavioral analysis; and neural encoding, which relates 3D behavioral features to simultaneously recorded neural activity. BEAST3D thus establishes a versatile framework for behavioral analysis that leverages 3D structure in modern multi-view laboratory recordings.

Feed-forward transformer models have driven remarkable progress in 3D vision, yet their quadratic complexity renders them impractical for long video sequences. Streaming 3D reconstruction addresses this bottleneck by processing frames sequentially with constant memory. However, existing recurrent architectures inevitably suffer from progressive degradation over long sequences, as they fail to account for the intrinsic reliability of observations and the accumulated confidence of the historical state, leading to severe error accumulation. To address these issues, we introduce BAT3R, a training-free Bayesian adaptation framework comprising two synergistic modules: State Innovation Gating (SIG) and Bayesian-guided Adaptive State Updating (BASU). SIG acts as a distributional regularizer that anchors state evolution to its geometric initialization, effectively arresting representational drift during long-term inference. Building on this, BASU reformulates state update as a recursive Bayesian inference process within a Kalman filtering framework. By jointly modeling the temporal reliability of observations via information-theoretic gain and spatial relevance via cross-attention maps, BASU achieves an asymmetric spatio-temporal state fusion: well-consolidated historical regions are protected from redundant updates, while genuinely novel scene content is aggressively incorporated. Extensive experiments on standard benchmarks (7-Scenes, TUM, ScanNet, KITTI, etc.) demonstrate that BAT3R consistently outperforms state-of-the-art online 3D reconstruction methods, with the most significant gains observed on long sequences and complex scenes.

Feed-forward models for 3D reconstruction have achieved strong performance using deep cross-view attention to exchange information across images. However, these approaches often depend on heavy decoder stacks and lack a structured mechanism for geometry refinement, resulting in poor multi-view consistency. We address this by drawing inspiration from classical bundle adjustment (BA), which can be viewed as an iterative information propagation process between poses and local geometry. Inspired by BA, we propose BA-T, an iterative Transformer that implements BA-style structured updates as a repeatable layer in implicit token space. Instead of relying on deep attention stacks, BA-T refines predictions based on latent residual by a single lightweight layer. Experiments demonstrate that BA-T progressively improves pose and reconstruction accuracy across iterations, achieves stronger cross-view consistency than conventional decoders, and matches or surpasses substantially larger models while using only 16\% of their decoder parameters. BA-T provides a compact, efficient, and structural alternative to depth-heavy attention, enabling accurate 3D reconstruction within a lightweight architecture. The code will be made publicly.

A photorealistic and immersive human avatar experience demands capturing fine, person-specific details such as cloth and hair dynamics, subtle facial expressions, and characteristic motion patterns. Achieving this requires large, high-quality datasets, which often introduce ambiguities and spurious correlations when very similar poses correspond to different appearances. Models that fit these details during training can overfit and produce unstable, abrupt appearance changes for novel poses. We propose a 3D Gaussian Splatting avatar model with a spatial MLP backbone that is conditioned on both pose and an appearance latent. The latent is learned during training by an encoder, yielding a compact representation that improves reconstruction quality and helps disambiguate pose-driven renderings. At driving time, our predictor autoregressively infers the latent, producing temporally smooth appearance evolution and improved stability. Overall, our method delivers a robust and practical path to high-fidelity, stable avatar driving.

Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40$\textdegree{}$ tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight ($<2$M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures.

Public validation sets in machine-learning challenges frequently become de facto test sets in downstream work, making train–validation source overlap a first-order concern for claim validity. We audit the released public split of the *Capsule Vision 2024 Challenge* (CV2024) using perceptual hashing (pHash+dHash, PDQ at 99.4%), pixel NCC, and DINOv2/ResNet feature checks. The released CV2024 public split contains 1,381 / 11,581 KVASIR validation rows (11.9%; 540 pixel-confirmed at NCC ≥ 0.99, i.e. 4.66%) with pHash-exact training matches — directly violating the organizers' "NOT have duplicates" preprocessing description. All 1,381 trace to shared Kvasir-Capsule video prefixes, consistent with frame-level (not video-level) random splitting. The full CV2024-KVASIR slice falls in the disclosed Kvasir-Capsule near-duplicate set (60.7% bit-identical; non-KVASIR controls ≤ 0.31%). Training on the Kvasir-origin-removed le6 split drops public-validation balanced accuracy by Δ = −0.213 (95% CI [−0.220, −0.206], n=10 paired seeds), quantifying the public score's dependence on the KVASIR channel; the magnitude is mechanically expected given the 71.8% KVASIR validation composition rather than a leakage-specific causal effect. le6 is not a hidden-test proxy (team-level Spearman against AIIMS: ρ=0.355 vs. 0.566 for the original score). We release le6, le6_plus_internal, hash/NCC annotations, audit code, and a Croissant 1.1 descriptor as a within-public-pool sensitivity probe; no image bytes are redistributed. Detector portability: ISIC 2019 cross-source rate is 0.008% (2/25,331, both re-attributed as within-source metadata-gap pairs ⇒ genuine cross-source 0/25,331; Cassidy et al. 2022's recall-tuned multi-hash pipeline flags 14,310); HyperKvasir and Kvasir-SEG cross-benches against CV2024 return 0 pairs (procedure-disjoint negative controls). At the matched NCC ≥ 0.99 endpoint, the CV2024 rate of 4.66% (540/11,581) is ≈ 148–583× above the ISIC cross-source rate (Wilson-lower / Wilson-upper to point/point).

A recent work shows that Attention Transfer, which transfers only the attention patterns from a pre-trained teacher Vision Transformer (ViT) to a randomly initialized standard student ViT, is sufficient to recover the full benefit of the teacher's pre-trained weights. We revisit this finding on a comprehensive benchmark of 20 teachers from 11 well-known ViT families and reveal that Attention Transfer is not universally effective. While 7 families transfer successfully, 4 consistently fail, falling up to 5.1\% below the from-scratch no-transfer baseline. Further results demonstrate that this failure is family-consistent across model sizes, and persists under extended training durations, different transfer datasets, and out-of-distribution evaluations. Controlled analyses then consistently localize the problem to the attention-routing channel, indicating that the key issue is not whether the student can match the teacher's attention patterns, but whether the matched patterns remain functional for the student. Crucially, we identify architectural mismatch between the pre-trained teacher and the standard student as the primary mechanism. By adding only the teacher's native architectural components to the student in a randomly initialized state, we completely reverse the failure for all 4 families. Notably, these components alone do not improve from-scratch training, confirming that they specifically unlock the usability of the teacher's attention. We further systematically show that this failure is not explained by the inadequate choice of transfer loss or by differences in pre-training recipes. Our findings refine the prevailing understanding of attention in ViT representations: attention is sufficient only when the student architecture matches the teacher.

Visual Geometry Grounded Transformer (VGGT) advances 3D reconstruction via scalable Transformer architecture, but the quadratic complexity of global attention prevents long context application. StreamVGGT enables streaming with causal attention, yet its KV cache grows linearly with frames, causing memory overflow and quality degradation. We present RetrieveVGGT, a training-free framework, which formulates context construction for VGGT as a retrieval problem. By retrieving a fixed number of relevant frames at each step, VGGT maintains a controllable memory budget, which is close to its training context length. Interestingly, we find that the similarity between current frame queries and cached history frame keys at the first global attention layer of VGGT is already a strong indicator of relevance, eliminating the need for additional learned scoring. To enhance information diversity similar to a recommender system, we propose Segment Sampling so that the retrieval spans distinct relevant segments rather than a single high-similarity region. We design a pose-aware spatial memory mechanism that organizes history frames according to their already estimated camera poses, enabling location-aware retrieval. Extensive experiments demonstrate that RetrieveVGGT achieves state-of-the-art performance, outperforming StreamVGGT, TTT3R, and InfiniteVGGT while maintaining constant memory usage regardless of sequence length.

Recent feed-forward 3D Gaussian Splatting (3DGS) methods have enabled effective multi-view 3D reconstruction by predicting Gaussian primitives in a single forward pass. However, existing approaches predominantly adopt a pixel-aligned paradigm that predicts one Gaussian per input pixel, coupling the Gaussian count to the input resolution and number of views. This also leads to redundant primitives in textureless regions and insufficient coverage in geometrically complex areas. We present Anchor3DGS, a pose-free feed-forward 3DGS framework that replaces pixel-aligned prediction with a compact anchor-based representation. Guided by information entropy, our method places a set of 3D anchors in the scene, concentrating them in regions of higher visual complexity while maintaining broad spatial coverage. Each anchor performs occlusion-aware aggregation of multi-view image features, exchanges information with other anchors, and is decoded into a set of local Gaussians. A lightweight feature enhancement module further refines the coarse RGB output from Gaussian splatting and improves rendering fidelity. Extensive experiments on diverse benchmark datasets demonstrate that our Anchor3DGS achieves state-of-the-art novel view synthesis quality while only using an order of magnitude fewer Gaussian primitives.