REMODE: Probabilistic, monocular dense reconstruction in real time

By Matia Pizzoli, Christian Forster, Davide Scaramuzza. IEEE International Conference on Robotics and Automation, 2014

353 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/7a46ab45b960f2405bd6fd04ba5c171e1416755e

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In Media Technology

Citations in the corpus

Autonomous aerial navigation using monocular visual‐inertial fusion (2018, methodology): “By demonstrating challenging indoor environment experiments, the F IGURE 1 A synthetic and closeup of our aerial robot platform: (1) Perception core; (2) DJI Lightbridge 2; (3) DJI Phantom 4 Pro battery; (4) DJI Takyon Z425-M ESC; (5) DJI E310motors; (6) iPad Pro; (7) radio controller authors give a”
SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems (2017, methodology): “When a depth ﬁlter’s uncertainty becomes small enough, a new 3D point is inserted in the map and is immediately used for motion estimation.”
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, methodology): “Finally, our algorithm can be employed to obtain more accurate and reﬁned maps by applying some SfM or Multi-Stereo techniques [17], [46] in order to ﬁlter the structural lines, hence obtaining more meaningful information of the structured parts of the environment.”
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction (2017, methodology): “As for REMODE, since it requires as input the camera pose estimation at each frame, we use the trajectory and key-frames estimated by LSD-SLAM with bootstrapping.”
Direct Sparse Odometry (2016, methodology): “of an additional log-likelihood energy term [21], [22], [26].”
Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness (2017, background): “Other approaches have been developed that estimate depth using a monocular video stream [23], [34].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “These representations have recently gained popularity in monocular SLAM, in conjunction with the use of direct methods [118], [184], [203],”
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (2018, methodology): “Their method shows superior performance over monocular SLAM [9,30,45,35] on indoor datasets [15,38].”
State of the Art of Automated Buses (2018, neutral): “Considering that an autonomous vehicle would be moving, it is possible estimate the distance of objects by measuring the variations between two consecutive frames acquired from the same (moving) camera [44,45].”
LSD-SLAM: Large-Scale Direct Monocular SLAM (2014, methodology): “While direct image alignment is well-established for RGB-D or stereo sensors [14,4], only recently monocular direct VO algorithms have been proposed: In [24,20,21], accurate and fully dense depth maps are computed using a variational formulation, which however is computationally demanding and requir”
PL-SLAM: Real-time monocular visual SLAM with points and lines (2017, methodology): “To solve this, dense and direct methods can be applied, even though they are likely to be computationally expensive [19], [21], and require dedicated GPU-implementations to achieve real-time performance.”
Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision (2016, methodology): “A valid alternative to cope with real-world gaps would be to use monocular densereconstruction methods, such as REMODE [13]; however, they require more computing power (GPUs).”
Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle (2016, methodology): “Therefore, we use the REMODE (“Regularized Monocular Depth”) algorithm that we proposed in Pizzoli et al. (2014) and that we summarize in the following.”
Survey on Computer Vision for UAVs: Current Developments and Trends (2017, methodology): “In this work a monocular approach uses only an onboard smartphone processor for semi direct visual odometry [54], multi sensor fusion [56] and a modified version of Regularized Modular Depth Estimation (REMODE) [57].”
SVO: Fast semi-direct monocular visual odometry (2014, methodology): “In [29] we demonstrate how the same approach can be used for dense mapping.”