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Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, lack cross-domain transferability, while model-based methods, though more robust, are less accurate. To address the limitations of optical flow estimation, recent works have focused on normal flow, which can be more reliably measured in regions with limited texture or strong edges. However, existing normal flow estimators are predominantly model-based and suffer from high errors. In this paper, we propose a novel supervised point-based method for normal flow estimation that overcomes the limitations of existing event learning-based approaches. Using a local point cloud encoder, our method directly estimates per-event normal flow from raw events, offering multiple unique advantages: 1) It produces temporally and spatially sharp predictions. 2) It supports more diverse data augmentation, such as random rotation, to improve robustness across various domains. 3) It naturally supports uncertainty quantification via ensemble inference, which benefits downstream tasks. 4) It enables training and inference on undistorted data in normalized camera coordinates, improving transferability across cameras. Extensive experiments demonstrate that our method achieves better and more consistent performance than state-of-the-art methods when transferred across different datasets. Leveraging this transferability, we train our model on the union of datasets and release it for public use. Finally, we introduce an egomotion solver based on a maximum-margin problem that uses normal flow and IMU to achieve strong performance in challenging scenarios. Codes are available at github.com/dhyuan99/VecKM flow.
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Approximate Differentiable Likelihoods for Astroparticle Physics Experiments
Traditionally, inference in liquid xenon direct detection dark matter experiments has used estimators of event energy or density estimation of simulated data. Such methods have drawbacks compared to the computation of explicit likelihoods, such as an inability to conduct statistical inference in high-dimensional parameter spaces, or a failure to make use of all available information. In this work, we implement a continuous approximation of an event simulator model within a probabilistic programming framework, allowing for the application of high performance gradient-based inference methods such as the No-U-Turn Sampler. We demonstrate an improvement in inference results, with percent-level decreases in measurement uncertainties. Finally, in the case where some observables can be measured using multiple independent channels, such a method also enables the incorporation of additional information seamlessly, allowing for full use of the available information to be made.
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- Award ID(s):
- 2046549
- PAR ID:
- 10615391
- Publisher / Repository:
- ACAT 2024
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
