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TransNormal-2 improves monocular normal estimation by correcting VAE reconstruction errors with geometry-aware training losses and a lightweight RGB-guided refinement module, achieving strong results with minimal annotations.
The paper proposes GeoLAMP, a geometry-aware latent autoregressive generative model for solving multiphysics partial differential equations in complex geometries, using a dual-encoder architecture and causal self-attention transformer with flow matching for stable and scalable predictions.
The paper investigates the geometric challenges in merging differentially private task models and introduces DP-Merging, a framework to enhance mergeability while maintaining privacy guarantees.
Presents GeoIncNO, a geometry-aware incremental neural operator that improves long-horizon PDE prediction via residual latent increments and mean-fluctuation decoupled reconstruction, achieving better stability and spectral fidelity on 1D/2D/3D benchmarks.
KLQ is a training-free LLM quantization method that allocates bits per direction based on measured KL divergence, outperforming existing training-free rotation-based methods on W4A4KV4-bit settings for models like Llama 3.2 1B and Qwen 2.5.
This paper proposes SG-WAM, a self-guided framework for learning geometry-aware action-conditioned world models directly in policy-derived representation space. It achieves state-of-the-art success rates on LIBERO and LIBERO-Plus benchmarks, outperforming strong baselines in real-world evaluations.
SUFLECA is a weakly-supervised framework for zero-shot CAD-to-image alignment, achieving state-of-the-art accuracy on ScanNet25k by scaling up geometry-grounded feature learning from pretrained visual representations.
Introduces SpaR3D-MoE, an end-to-end framework for adaptive 3D spatial reasoning from sparse RGB views, using manifold sampling and geometry-inductive mixture-of-experts to achieve state-of-the-art performance on VSI-Bench, ScanQA, and SQA3D.
This paper presents GAIA, a geometry-aware learning framework for UWB denoising and work-zone reconstruction that couples temporal range modeling with latent anchor-layout estimation. Evaluated on real-world outdoor data, GAIA reduces range MSE by 18.4% and improves polygon IoU by 15.5% over baselines, demonstrating effective boundary-level reconstruction under NLOS conditions.
PixWorld presents a unified pixel-space diffusion approach for 3D scene reconstruction and generation, overcoming limitations of latent-space methods by using direct image-level supervision and geometry-aware feature alignment. The method outperforms prior generation methods and matches state-of-the-art reconstruction methods.
This paper proposes a geometry-aware multi-support heterogeneous graph neural network for fine-scale rainfall field reconstruction, which fuses observations from point gauges, path-integrated microwave links, and gridded radar/satellite data. The method reduces RMSE by 23.2% over classical interpolation on Singapore data and shows greatest gains when the field is undersampled relative to its spatial correlation length.
Proposes Geometry-aware R-Structured KAN (GRS-KAN), a hybrid neural architecture that integrates R-functions into KAN to encode geometric and logical constraints, achieving up to 67% RMSE reduction on regression benchmarks with discontinuities.
RaysUp is an ultra-lightweight, task-agnostic feature upsampling framework that uses geometry-aware ray domain techniques to reconstruct high-resolution features from low-resolution VFM outputs, achieving state-of-the-art performance with 84% fewer parameters than prior work and 7x faster inference.
Proposes REEF-GP, a post-hoc uncertainty quantification framework that fits a Gaussian process to the residuals of a frozen neural operator using its internal embeddings, enabling geometry-aware and calibrated uncertainties at low cost.
OmniLoc is a geometry-aware foundation model for anchor-free user equipment localization across diverse indoor environments, using a unified tokenization module, a geometry-aware Transformer, and geometric embeddings to significantly outperform existing methods.
GRASP introduces a geometry-aware, interaction-based method for scalable pretraining data attribution that models subset dynamics, outperforming existing additive approaches by over double the task-level rank correlation while reducing computation costs.
Introduces GARD, a diffusion-based framework that operates in the feature space of a feed-forward 3D reconstructor to jointly recover scene geometry and high-quality imagery from degraded inputs.
This paper introduces geometry-aware flow matching for natural images by treating them as points on a hypersphere, proposing SOT-CFM and SFM methods that improve generative modeling by leveraging the spherical structure of image data.
AnyMo is a geometry-aware framework for setup-agnostic human motion modeling using physics-grounded IMU simulation and graph encoding, achieving significant improvements in zero-shot activity recognition, cross-modal retrieval, and motion captioning across multiple datasets.
PanoWorld introduces spherical spatial cross-attention for panoramic reasoning, addressing limitations of MLLMs in 360-degree spatial understanding. It builds a large-scale pipeline for geometry-aware supervision and proposes a diagnostic benchmark, achieving state-of-the-art results on multiple benchmarks.