NeurIPS 2026 Papers

Visual Foundation Models (VFMs) have demonstrated strong capabilities in visual-geometric tasks such as image matching and 3D reconstruction. Recent work has revealed the potential of VFMs in label-free indoor RGB-D registration, where the visual modality is intrinsic to the task. A natural question follows: can this success extend to more challenging outdoor scenarios characterized by sparse, irregular LiDAR data with only partial visual coverage? We answer this with PROMOTER, a novel teacher–student framework for unsupervised LiDAR registration that bridges visual priors and sparse LiDAR geometry. To leverage pre-trained geometric priors for 3D registration, we must disentangle them from confounding signals introduced by modality and task gaps under partial visual coverage. To this end, we introduce a lightweight Adaptive Prior Integrator that extracts and integrates these priors into an enhanced teacher, together with a Perceptual-Geometric Labeler that produces high-quality pseudo-labels. For student training, we propose Matchability-Anchored Training to improve robustness under noisy supervision. Experiments on KITTI and nuScenes demonstrate state-of-the-art performance and improved scalability across registration models and VFMs, while incurring no additional parameters at inference. Code will be released.

Joint classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data benefits from complementary spectral--geometric cues, but existing fusion methods largely assume locally consistent cross-modal features and symmetric pixel-wise trust, which is often violated in real urban scenes. We term this pixel-level phenomenon \emph{cross-modal local inconsistency} (CMI) and quantify it with a Strong-Edge CMI Ratio: dual-modal gradient analysis shows that over $16\%$ of strong-edge pixels exhibit single-modality dominance across three benchmarks, while ROI-level analysis reveals the class-entanglement induced by symmetric fusion. To address this issue, we propose \textbf{CMI-Trans} (\textbf{C}ross-\textbf{M}odal \textbf{I}nconsistency-aware \textbf{Trans}port), which promotes per-pixel reliability modeling from a post-hoc diagnostic to a structural signal for alignment and training. CMI-Trans combines Energy-Calibrated Reliability (ECR), Uncertainty-Shaped Transport Alignment (USTA) with Sinkhorn-regularized optimal transport, and Triadic Co-Optimization (TCO) that jointly optimizes classification, uncertainty consistency, and alignment stability on a Mamba-based fusion backbone. On Houston, Trento, and MUUFL, CMI-Trans achieves competitive overall accuracy and substantial gains on CMI-affected classes (e.g., $+17.90\%$ on Houston C10). The ECR-derived uncertainty score also separates reliable from erroneous predictions ($\rho(U,e)<0$; top-$20\%$ highest-$U$ error rate $\le 0.18\%$), while improving feature separability in shadow-induced CMI regions. These results show that explicitly modeling CMI through uncertainty-shaped adaptive alignment yields more accurate and interpretable multimodal remote sensing classification.

In large-scale outdoor LiDAR localization, Scene Coordinate Regression (SCR) achieves sub-meter accuracy, but its deployment in high-precision autonomous driving is hindered by a "last-mile" problem: the point-wise prediction paradigm causes unphysical trajectory jittering, rendering localization kinematically discontinuous despite low mean errors. To bridge the gap between high-precision localization and temporal kinematic consistency, we propose BiLi, a spatiotemporal synergistic relocalization framework. Spatially, BiLi utilizes Implicit Manifold Regularization (ISR) driven by an offline neural implicit field. Formulated as a novel point-to-manifold projection constraint, ISR strictly anchors predicted coordinates to continuous physical zero-level sets. Temporally, to bypass computationally heavy implicit optimization, we distill the teacher's kinematics into a lightweight feed-forward network, compelling it to learn robust, equivariant physical priors. Finally, a Differentiable Robust Manifold Gating mechanism dynamically fuses the global spatial predictions with the temporal kinematic states via adaptive state updating, facilitating efficient real-time deployment. Extensive experiments demonstrate that BiLi fundamentally resolves the "last-mile" challenge. Our method outperforms state-of-the-art approaches by 25% and 65% on the Oxford and NCLT benchmarks, respectively, delivering drift-free, kinematically coherent, and highly accurate real-time localization.

Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language models (LLMs) to reason about smell. The benchmark contains 1,010 questions across eight task categories spanning odor classification, odor primary descriptor identification, intensity and pleasantness judgments, multi-descriptor prediction, mixture similarity, olfactory receptor activation, and smell identification from real-world odor sources. Each question is presented in two prompt formats, compound names and isomeric SMILES, to evaluate the effect of molecular representations. Evaluating 21 model configurations across major model families, we find that compound-name prompts consistently outperform isomeric SMILES, with gains ranging from +3.1 to +18.9 percentage points (mean $\approx$ +7 points), suggesting current LLMs access olfactory knowledge primarily through lexical associations rather than structural molecular reasoning. The best-performing model reaches 64.4\% overall accuracy, which highlights both emerging capabilities and substantial remaining gaps in olfactory reasoning. We further evaluate a subset of the OP across 21 languages and find that aggregating predictions across languages improves olfactory prediction, with AUROC=0.86 for the best performing language ensemble model. LLMs should be able to handle olfactory and not just visual or auditory information.