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Register a new userResidual 3D Scene Flow Learning with Context-Aware Feature Extraction
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Guangming Wang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Scene flow estimation is the task to predict the point-wise 3D displacement vector between two consecutive frames of point clouds, which has important application in fields such as service robots and autonomous driving. Although many previous works have explored greatly on scene flow estimation based on point clouds, we point out two problems that have not been noticed or well solved before: 1) Points of adjacent frames in repetitive patterns may be wrongly associated due to similar spatial structure in their neighbourhoods; 2) Scene flow between adjacent frames of point clouds with long-distance movement may be inaccurately estimated. To solve the first problem, we propose a novel context-aware set conv layer to exploit contextual structure information of Euclidean space and learn soft aggregation weights for local point features. Our design is inspired by human perception of contextual structure information during scene understanding. We incorporate the context-aware set conv layer in a context-aware point feature pyramid module of 3D point clouds for scene flow estimation. For the second problem, we propose an explicit residual flow learning structure in the residual flow refinement layer to cope with long-distance movement. The experiments and ablation study on FlyingThings3D and KITTI scene flow datasets demonstrate the effectiveness of each proposed component and that we solve problem of ambiguous inter-frame association and long-distance movement estimation. Quantitative results on both FlyingThings3D and KITTI scene flow datasets show that our method achieves state-of-the-art performance, surpassing all other previous works to the best of our knowledge by at least 25%.
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Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control. Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents. We add this constraint as a regularization term to our proposed network by defining a pixel-level photometric loss and an image-level structural similarity loss. To benchmark our method, different challenging scenes including indoor and outdoor environment are tested with our proposed approach and state-of-the-arts. And the experimental results demonstrate significant performance improvement of our method on both prediction accuracy and convergence efficiency.
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