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1. 3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection

   Ge, Yunhao; Yu, Hong-Xing; Zhao, Cheng; Guo, Yuliang; Huang, Xinyu; Ren, Liu; Itti, Laurent; Wu, Jiajun (December 2023, NeurIPS 2023)

   A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets. While augmenting real scenes with virtual objects holds promise to improve both the diversity and quantity of the objects, it remains elusive due to the lack of an effective 3D object insertion method in complex real captured scenes. In this work, we study augmenting complex real indoor scenes with virtual objects for monocular 3D object detection. The main challenge is to automatically identify plausible physical properties for virtual assets (e.g., locations, appearances, sizes, etc.) in cluttered real scenes. To address this challenge, we propose a physically plausible indoor 3D object insertion approach to automatically copy virtual objects and paste them into real scenes. The resulting objects in scenes have 3D bounding boxes with plausible physical locations and appearances. In particular, our method first identifies physically feasible locations and poses for the inserted objects to prevent collisions with the existing room layout. Subsequently, it estimates spatially-varying illumination for the insertion location, enabling the immersive blending of the virtual objects into the original scene with plausible appearances and cast shadows. We show that our augmentation method significantly improves existing monocular 3D object models and achieves state-of-the-art performance. For the first time, we demonstrate that a physically plausible 3D object insertion, serving as a generative data augmentation technique, can lead to significant improvements for discriminative downstream tasks such as monocular 3D object detection. 

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2. Absolute-ROMP: Recovering Multi-person 3D Poses and Shapes with Absolute Scales from a Single RGB Image

   Abdulrahman, B; Zhu, Z (August 2024, springer)

   One of the grand challenges in computer vision is to recover 3D poses and shapes of multiple human bodies with absolute scales from a single RGB image. The challenge stems from the inherent depth and scale ambiguity from a single view. The state of the art on 3D human pose and shape estimation mainly focuses on estimating the 3D joint locations relative to the root joint, defined as the pelvis joint. In this paper, a novel approach called Absolute-ROMP is proposed, which builds upon a one-stage multi-person 3D mesh predictor network, ROMP, to estimate multi-person 3D poses and shapes, but with absolute scales from a single RGB image. To achieve this, we introduce absolute root joint localization in the camera coordinate frame, which enables the estimation of 3D mesh coordinates of all persons in the image and their root joint locations normalized by the focal point. Moreover, a CNN and transformer hybrid network, called TransFocal, is proposed to predict the focal length of the image’s camera. This enables Absolute-ROMP to obtain absolute depth information of all joints in the camera coordinate frame, further improving the accuracy of our proposed method. The Absolute-ROMP is evaluated on the root joint localization and root-relative 3D pose estimation tasks on publicly available multi-person 3D pose datasets, and TransFocal is evaluated on a dataset created from the Pano360 dataset. Our proposed approach achieves state-of-the-art results on these tasks, outperforming existing methods or has competitive performance. Due to its real-time performance, our method is applicable to in-the-wild images and videos. 

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3. FlowCam: Training generalizable 3D radiance fields without camera poses via pixel-aligned scene flow

   Smith, Cameron; Du, Yilun; Tewari, Ayush; Sitzmann, Vincent (December 2023, Neural Information Processing Systems)

   Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion, which is prohibitively expensive to run at scale. We propose a method that jointly reconstructs camera poses and 3D neural scene representations online and in a single forward pass. We estimate poses by first lifting frame-to-frame optical flow to 3D scene flow via differentiable rendering, preserving locality and shift-equivariance of the image processing backbone. SE(3) camera pose estimation is then performed via a weighted least-squares fit to the scene flow field. This formulation enables us to jointly supervise pose estimation and a generalizable neural scene representation via re-rendering the input video, and thus, train end-to-end and fully self-supervised on real-world video datasets. We demonstrate that our method performs robustly on diverse, real-world video, notably on sequences traditionally challenging to optimization-based pose estimation techniques. 

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4. FlowCam: Training generalizable 3D radiance fields without camera poses via pixel-aligned scene flow

   Smith, Cameron; Du, Yilun; Tewari, Ayush; Sitzmann, Vincent (December 2023, Neural Information Processing Systems)

   Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. The key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion, which is prohibitively expensive to run at scale. We propose a method that jointly reconstructs camera poses and 3D neural scene representations online and in a single forward pass. We estimate poses by first lifting frame-to-frame optical flow to 3D scene flow via differentiable rendering, preserving locality and shift-equivariance of the image processing backbone. SE(3) camera pose estimation is then performed via a weighted least-squares fit to the scene flow field. This formulation enables us to jointly supervise pose estimation and a generalizable neural scene representation via re-rendering the input video, and thus, train end-to-end and fully self-supervised on real-world video datasets. We demonstrate that our method performs robustly on diverse, real-world video, notably on sequences traditionally challenging to optimization-based pose estimation techniques. 

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5. Weakly Supervised 2D Pose Adaptation and Body Part Segmentation for Concealed Object Detection

   https://doi.org/10.3390/s23042005  

   Amadi, Lawrence; Agam, Gady (February 2023, Sensors)

   Weakly supervised pose estimation can be used to assist unsupervised body part segmentation and concealed item detection. The accuracy of pose estimation is essential for precise body part segmentation and accurate concealed item detection. In this paper, we show how poses obtained from an RGB pretrained 2D pose detector can be modified for the backscatter image domain. The 2D poses are refined using RANSAC bundle adjustment to minimize the projection loss in 3D. Furthermore, we show how 2D poses can be optimized using a newly proposed 3D-to-2D pose correction network weakly supervised with pose prior regularizers and multi-view pose and posture consistency losses. The optimized 2D poses are used to segment human body parts. We then train a body-part-aware anomaly detection network to detect foreign (concealed threat) objects on segmented body parts. Our work is applied to the TSA passenger screening dataset containing millimeter wave scan images of airport travelers annotated with only binary labels that indicate whether a foreign object is concealed on a body part. Our proposed approach significantly improves the detection accuracy of TSA 2D backscatter images in existing works with a state-of-the-art performance of 97% F1-score, 0.0559 log-loss on the TSA-PSD test-set, and a 74% reduction in 2D pose error. 

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