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1. 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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2. 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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3. Learning neural representation of camera pose with matrix representation of pose shift via view synthesis

   Zhu, Y.; Gao, R.; Huang, S.; Zhu, S. C.; Wu, Y. N. (January 2021, IEEE Conference on Computer Vision and Pattern Recognition)

   How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression. 

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4. VehiPose: a multi-scale framework for vehicle pose estimation

   https://doi.org/10.1117/12.2595800  

   Gupta, Divyansh; Artacho, Bruno; Savakis, Andreas (July 2021, Applications of Digital Image Processing XLIV)

   Tescher, Andrew G.; Ebrahimi, Touradj (Ed.)

   Vehicle pose estimation is useful for applications such as self-driving cars, traffic monitoring, and scene analysis. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach that achieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. Our approach aims to reduce the loss due to successive pooling layers and preserve the multiscale contextual and spatial information in the encoder feature representations. The waterfall module generates multiscale features, as it leverages the efficiency of progressive filtering while maintaining wider fields-of-view through the concatenation of multiple features. This multi-scale approach results in a robust vehicle pose estimation architecture that incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network. 

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5. 3D Reconstruction with Fast Dipole Sums

   https://doi.org/10.1145/3687914  

   Chen, Hanyu; Miller, Bailey; Gkioulekas, Ioannis (November 2024, ACM Transactions on Graphics)

   We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and radiance fields as per-point attributes of a dense point cloud, which we initialize from structure from motion. We additionally derive Barnes-Hut fast summation schemes for accelerated forward and adjoint dipole sum queries. These queries facilitate the use of ray tracing to efficiently and differentiably render images with our point-based representations, and thus update their point attributes to optimize scene geometry and appearance. We evaluate our method in inverse rendering applications against state-of-the-art alternatives, based on ray tracing of neural representations or rasterization of Gaussian point-based representations. Our method significantly improves 3D reconstruction quality and robustness at equal runtimes, while also supporting more general rendering methods such as shadow rays for direct illumination. 

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