GeoRefine: Self-supervised Online Depth Refinement for Accurate Dense Mapping

Ji, Pan; Yan, Qingan; Ma, Yuxin; Xu, Yi

doi:10.1007/978-3-031-19769-7_21

Cited by 7 publications

(2 citation statements)

References 55 publications

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“…In a followup work by the same authors [14], the PoseNet is substituted with optical flow-based point matching. Similarly, GeoRefine [9] combines online depth refinement with dense visual mapping. While the DepthNet is updated following the aforementioned works, GeoRefine uses a non-adaptive odometry and tracking module based on optical flow.…”

Section: Related Workmentioning

confidence: 99%

Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

Vödisch

Cattaneo

Burgard

et al. 2023

Springer Proceedings in Advanced Robotics

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Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In this work, we introduce CoVIO for online continual learning of visual-inertial odometry. CoVIO effectively adapts to new domains while mitigating catastrophic forgetting by exploiting experience replay. In particular, we propose a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices. We further provide an asynchronous version that decouples the odometry estimation from the network weight update step enabling continuous inference in real time. We extensively evaluate CoVIO on various real-world datasets demonstrating that it successfully adapts to new domains while outperforming previous methods. The code of our work is publicly available at http://continual-slam.cs.uni-freiburg.de.

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Section: Related Workmentioning

confidence: 99%

Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

Vödisch

Cattaneo

Burgard

et al. 2023

Springer Proceedings in Advanced Robotics

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“…Depth estimation with auxiliary tasks. In self-supervised monocular depth estimation, auxiliary tasks such as segmentation [31], simultaneous localization and mapping (SLAM) [32], optical flow [32], [33], multiscale fusion [34], and relative depth estimation [35], are incorporated to help improve correspondence between multiple frames [32], [33] or maintain consistency between global structures and local details [31], [34], [35].…”

Section: Monocular Depth Estimationmentioning

confidence: 99%

Monocular Depth Estimation for Glass Walls With Context: A New Dataset and Method

Liang,

Deng,

Liu

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.

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