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20242026
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cs.CV2026

Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images

Matias Turkulainen, Akshay Krishnan, Filippo Aleotti +6

We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level AND by satellite. Faithful reconstruct…

cs.CV2026

Latent-Compressed Variational Autoencoder for Video Diffusion Models

Jiarui Guan, Wenshuai Zhao, Zhengtao Zou +2

Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-quality video reconstruction.…

cs.CV2026

PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos

Yihao Wang, Yang Miao, Wenshuai Zhao +8

Articulation perception aims to recover the motion and structure of articulated objects (e.g., drawers and cupboards), and is fundamental to 3D scene understanding in robotics, sim…

cs.CV2026

Post-hoc Probabilistic Vision-Language Models

Anton Baumann, Rui Li, Marcus Klasson +5

Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map image…

cs.CV2025

FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D Scene Reconstruction and Benchmarking

Ulas Gunes, Matias Turkulainen, Xuqian Ren +3

The development of large-scale 3D scene reconstruction and novel view synthesis methods mostly rely on datasets comprising perspective images with narrow fields of view (FoV). Whil…

cs.CV2025

DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering

Yihao Wang, Marcus Klasson, Matias Turkulainen +3

Gaussian splatting enables fast novel view synthesis in static 3D environments. However, reconstructing real-world environments remains challenging as distractors or occluders brea…