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1. Uncertainty-aware Self-supervised 3D Data Association

   Wang, Jianren; Ancha, Siddharth; Chen, Yi-Ting; Held, David (January 2020, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association. Large scale annotations for unlabeled data are cheaply obtained by automatic object detection and association across frames. We show how these self-supervised annotations can be used in a principled manner to learn point-cloud embeddings that are effective for 3D tracking. We estimate and incorporate uncertainty in self-supervised tracking to learn more robust embeddings, without needing any labeled data. We design embeddings to differentiate objects across frames, and learn them using uncertainty-aware self-supervised training. Finally, we demonstrate their ability to perform accurate data association across frames, towards effective and accurate 3D tracking. 

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2. Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

   You, Yurong; Phoo, Cheng Perng; Luo, Katie; Zhang, Travis; Chao, Wei-Lun; Hariharan, Bharath; Campbell, Mark; Weinberger, Kilian Q. (January 2022, Advances in neural information processing systems)

   For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment---ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (eg, unlabeled LiDAR point clouds) collected from the end-users' environments (ie target domain) to adapt the system to the difference between training and testing environments. While extensive research has been done on such an unsupervised domain adaptation problem, one fundamental problem lingers: there is no reliable signal in the target domain to supervise the adaptation process. To overcome this issue we observe that it is easy to collect unsupervised data from multiple traversals of repeated routes. While different from conventional unsupervised domain adaptation, this assumption is extremely realistic since many drivers share the same roads. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain. Concretely, we generate pseudo-labels with the out-of-domain detector but reduce false positives by removing detections of supposedly mobile objects that are persistent across traversals. Further, we reduce false negatives by encouraging predictions in regions that are not persistent. We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving. 

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3. Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection

   You, Yurong; Diaz-Ruiz, Carlos A.; Wang, Yan; Chao, Wei-Lun; Hariharan, Bharath; Campbell, Mark; Weinberger, Kilian Q. (April 2022, Proceedings of the IEEE International Conference on Robotics and Automation)

   Self-driving cars must detect other traffic partici- pants like vehicles and pedestrians in 3D in order to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit domain idiosyncrasies, making them fail in new environments—a serious problem for the robustness of self-driving cars. In this paper, we propose a novel learning approach that reduces this gap by fine-tuning the detector on high-quality pseudo-labels in the target domain – pseudo- labels that are automatically generated after driving based on replays of previously recorded driving sequences. In these replays, object tracks are smoothed forward and backward in time, and detections are interpolated and extrapolated— crucially, leveraging future information to catch hard cases such as missed detections due to occlusions or far ranges. We show, across five autonomous driving datasets, that fine-tuning the object detector on these pseudo-labels substantially reduces the domain gap to new driving environments, yielding strong improvements detection reliability and accuracy. 

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4. Real-Time 3D Object Detection and Recognition using a Smartphone [Real-Time 3D Object Detection and Recognition using a Smartphone]

   https://doi.org/10.5220/0011060600003209  

   Chen, Jin; Zhu, Zhigang (January 2022, Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE)

   Real-time detection of 3D obstacles and recognition of humans and other objects is essential for blind or low- vision people to travel not only safely and independently but also confidently and interactively, especially in a cluttered indoor environment. Most existing 3D obstacle detection techniques that are widely applied in robotic applications and outdoor environments often require high-end devices to ensure real-time performance. There is a strong need to develop a low-cost and highly efficient technique for 3D obstacle detection and object recognition in indoor environments. This paper proposes an integrated 3D obstacle detection system implemented on a smartphone, by utilizing deep-learning-based pre-trained 2D object detectors and ARKit- based point cloud data acquisition to predict and track the 3D positions of multiple objects (obstacles, humans, and other objects), and then provide alerts to users in real time. The system consists of four modules: 3D obstacle detection, 3D object tracking, 3D object matching, and information filtering. Preliminary tests in a small house setting indicated that this application could reliably detect large obstacles and their 3D positions and sizes in the real world and small obstacles’ positions, without any expensive devices besides an iPhone. 

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5. 3D Fiber Segmentation with Deep Center Regression and Geometric Clustering

   Aguilar, Camilo; Comer, Mary; Hanhan, Imad; Agyei, Ronald; Sangid, Michael (January 2021, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.)

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   Material and biological sciences frequently generate large amounts of microscope data that require 3D object level segmentation. Often, the objects of interest have a common geometry, for example spherical, ellipsoidal, or cylindrical shapes. Neural networks have became a popular approach for object detection but they are often limited by their training dataset and have difficulties adapting to new data. In this paper, we propose a volumetric object detection approach for microscopy volumes comprised of fibrous structures by using deep centroid regression and geometric regularization. To this end, we train encoder-decoder networks for segmentation and centroid regression. We use the regression information combined with prior system knowledge to propose cylindrical objects and enforce geometric regularization in the segmentation. We train our networks on synthetic data and then test the trained networks in several experimental datasets. Our approach shows competitive results against other 3D segmentation methods when tested on the synthetic data and outperforms those other methods across different datasets. 

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