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1. 3D SceneFlowNet: Self-supervised 3D Scene Flow Estimation based on Graph CNN

   Lu, Yawen ; Zhu, Yuhao ; Lu, Guoyu ( January 2021 , IEEE International Conference on Image Processing (ICIP))

   Despite deep learning approaches have achieved promising successes in 2D optical flow estimation, it is a challenge to accurately estimate scene flow in 3D space as point clouds are inherently lacking topological information. In this paper, we aim at handling the problem of self-supervised 3D scene flow estimation based on dynamic graph convolutional neural networks (GCNNs), namely 3D SceneFlowNet. To better learn geometric relationships among points, we introduce EdgeConv to learn multiple-level features in a pyramid from point clouds and a self-attention mechanism to apply the multi-level features to predict the final scene flow. Our trained model can efficiently process a pair of adjacent point clouds as input and predict a 3D scene flow accurately without any supervision. The proposed approach achieves superior performance on both synthetic ModelNet40 dataset and real LiDAR scans from KITTI Scene Flow 2015 datasets. 

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2. Self-supervised 4D Spatio-temporal Feature Learning via Order Prediction of Sequential Point Cloud Clips

   Wang, H ; Yang, L ; Rong, X ; Feng, J ; Tian, Y. ( June 2021 , Winter Conference on Applications of Computer Vision (WACV), 2021.)

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   Recently 3D scene understanding attracts attention for many applications, however, annotating a vast amount of 3D data for training is usually expensive and time consuming. To alleviate the needs of ground truth, we propose a self-supervised schema to learn 4D spatio-temporal features (i.e. 3 spatial dimensions plus 1 temporal dimension) from dynamic point cloud data by predicting the temporal order of sampled and shuffled point cloud clips. 3D sequential point cloud contains precious geometric and depth information to better recognize activities in 3D space compared to videos. To learn the 4D spatio-temporal features, we introduce 4D convolution neural networks to predict the temporal order on a self-created large scale dataset, NTU- PCLs, derived from the NTU-RGB+D dataset. The efficacy of the learned 4D spatio-temporal features is verified on two tasks: 1) Self-supervised 3D nearest neighbor retrieval; and 2) Self-supervised representation learning transferred for action recognition on smaller 3D dataset. Our extensive experiments prove the effectiveness of the proposed self-supervised learning method which achieves comparable results w.r.t. the fully-supervised methods on action recognition on MSRAction3D dataset. 

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3. HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds

   Gu, Xiuye ; Wang, Yijie ; Wu, Chongruo ; Lee, Yong Jae ; Wang, Panqu ( January 2019 , IEEE Conference on Computer Vision and Pattern Recognition (CVPR))

   We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, and fuse information from two consecutive point clouds. Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost. Our model can efficiently process a pair of point cloud frames at once with a maximum of 86K points per frame. Our approach achieves state-of-the-art performance on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Moreover, trained on synthetic data, our approach shows great generalization ability on real-world data and on different point densities without fine-tuning. 

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4. DiffPoseNet: Direct Differentiable Camera Pose Estimation

   Parameshwara, Chethan M. ; Hari, Gokul ; Fermuller, Cornelia ; Sanket, Nitin ; Aloimonos, Yiannis ( January 2022 , IEEE Conference on Computer Vision and Pattern Recognition)

   Current deep neural network approaches for camera pose estimation rely on scene structure for 3D motion estimation, but this decreases the robustness and thereby makes cross-dataset generalization difficult. In contrast, classical approaches to structure from motion estimate 3D motion utilizing optical flow and then compute depth. Their accuracy, however, depends strongly on the quality of the optical flow. To avoid this issue, direct methods have been proposed, which separate 3D motion from depth estimation, but compute 3D motion using only image gradients in the form of normal flow. In this paper, we introduce a network NFlowNet, for normal flow estimation which is used to enforce robust and direct constraints. In particular, normal flow is used to estimate relative camera pose based on the cheirality (depth positivity) constraint. We achieve this by formulating the optimization problem as a differentiable cheirality layer, which allows for end-to-end learning of camera pose. We perform extensive qualitative and quantitative evaluation of the proposed DiffPoseNet’s sensitivity to noise and its generalization across datasets. We compare our approach to existing state-of-the-art methods on KITTI, TartanAir, and TUM-RGBD datasets. 

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5. MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

   Huang, Jiahui ; Wang, He ; Birdal, Tolga ; Sung, Minhyuk ; Arrigoni, Federica ; Hu, Shi-Min ; Guibas, Leonidas ( January 2021 , Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition)

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   We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts; and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds. 

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