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We are publishing our second deep dive today as a follow-up post on SLAM and VIO in egocentric tracking. We go deep into the sensor tradeoffs b/w global shutter and rolling shutter and their implications on SLAM / VIO - specifically how the way the camera reads each frame... show more
16,732 просмотров • 3 месяцев назад •via X (Twitter)
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![This seemingly obvious prediction didn't take long to become reality. MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors Contributions: • The first real-time SLAM system using the two-view 3D reconstruction prior MASt3R [20] as a foundation. • Efficient techniques for pointmap matching, tracking and local fusion, graph construction and loop closure, and second-order global optimization. • A state-of-the-art dense SLAM system capable of handling generic, time-varying camera models. Abstract: We present a real-time monocular dense SLAM system, designed from the ground up using MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system remains robust on in-the-wild video sequences, making no assumptions on a fixed or parametric camera model beyond a unique camera center. Key features include: - Efficient methods for pointmap matching, camera tracking, and local fusion - Graph construction and loop closure - Second-order global optimization With known calibration, a simple modification achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.](https://image.24vids.com/tw-1868933842280604139/ext_tw_video_thumb/1868932917897871360/pu/img/WAmSelTOk2HJLm86.jpg)
![[SIGGRAPH 2025] Photoreal Scene Reconstruction from an Egocentric Device Contributions: 1. We address the importance of employing visual-inertial bundle adjustment (VIBA) that accounts for the rolling-shutter behavior of the RGB camera. This provides a continuous camera trajectory to model pixel movement in neural reconstruction. Our experiments demonstrate that using VIBA consistently improves the novel view quality in Gaussian Splatting by +1 dB in PSNR. 2. We introduce a rasterization-based image formulation pipeline that addresses common artifacts in physical image formation, including rolling shutter, lens shading, exposure, and gain compensation. Our approach is distinct in that we represent image poses as posed pixel arrays sampled from a continuous trajectory, rather than assigning a single camera pose per image, and preserve the merit of Gaussian rasterization. Unlike existing methods that require ray-tracing Gaussians, e.g., [Moenne-Loccoz et al. 2024], our formulation is applicable to general-purpose rasterization-based Gaussian splatting. When applied to 3D Gaussian Splatting (3DGS) [Kerbl et al. 2023], our approach can further enhance reconstruction quality by +1 dB. We outperform existing baselines and demonstrate a substantial quality improvement in handling complex scenes observed by egocentric devices. 3. To reduce the effect of blur from rapid head motion in darker indoor scenes, we propose a strategy of deliberately underexposing input videos during capture, inspired by HDR+ [Hasinoff et al. 2016]. We demonstrate that we can reconstruct high-quality, noise-free scene radiance from noisy, dim input videos, and further render sharp, blur-free videos at a higher dynamic range.](https://image.24vids.com/tw-1930869546531074148/amplify_video_thumb/1930869490562293760/img/AMxv5TbQ_LRSOLJz.jpg)
