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...
Mo-Yang Li, Zihan Zhu, Wei Zhang et al.· 0 citations
Multi-view computer vision pipelines typically rely on accurate sparse keypoints and robust descriptors. While incorporating line features has shown clear benefits for matching and pose estimation, existing point-line approaches remain inefficient: they detect points and lines separately, use increasingly heavy network...
François Costa, Raphael Kreft, Eckhard Goedeke et al.· 0 citations
Recovering metric 3D geometry from monocular images is a fundamental computer vision task, yet current methods remain heavily fragmented by fixed camera model assumptions and inflexible input schemes. We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors,...
Bo-Tao Ye, Marc Pollefeys, Ming-Hsuan Yang et al.· 1 citation
RIGOR is a large-scale reconstruction pipeline for gravity-aligned omnidirectional videos that retains a frozen feed-forward perspective backbone and exploits each panorama as a four-view virtual rig and improves both trajectory accuracy and reconstructed geometry over a feed-forward baseline on challenging constructio...
Ting-Jun Huang, Dmitry Rudshin, Mathieu Meyer et al.· 1 citation
This work proposes PolyLayout, a multi-room layout estimation method that parameterizes room layouts as Manhattan 3D polygons and optimizes them jointly across multiple rooms, and introduces two new multi-view multi-room layout benchmarks by providing layout annotations to existing datasets.
Gustav Hanning, Shao-Hui Liu, Rémi Pautrat et al.· 0 citations
GenRec is introduced, a multi-view flow matching model that builds the reconstruction--generation split directly into its architecture, supervision, and gradient flow, and attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved o...
Ata Çelen, Jaewoo Jung, Federico Tombari et al.· 0 citations
This work introduces a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos.
Zador Pataki, Paul-Edouard Sarlin, Marc Pollefeys· arXiv.org· 1 citation· ⚡1
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