Drones have been widely utilized in various fields, but the number of drones
being used illegally and for hazardous purposes has increased recently. To
prevent those illegal drones, in this work, we propose a novel framework for
reconstructing 3D trajectories of drones using a single camera. By leveraging
calibrated cameras, we exploit the relationship between 2D and 3D spaces. We
automatically track the drones in 2D images using the drone tracker and
estimate their 2D rotations. By combining the estimated 2D drone positions with
their actual length information and camera parameters, we geometrically infer
the 3D trajectories of the drones. To address the lack of public drone
datasets, we also create synthetic 2D and 3D drone datasets. The experimental
results show that the proposed methods accurately reconstruct drone
trajectories in 3D space, and demonstrate the potential of our framework for
single camera-based surveillance systems.

通过利用单个摄像头，我们提出了一种用于重构无人机的三维轨迹的新框架，并通过结合估计的二维无人机位置、实际长度信息和摄像头参数，几何推断了无人机的三维轨迹，进一步证明了我们的框架在基于单个摄像头的监控系统中的潜力。

使用单个摄像头重建无人机的三维轨迹

3D Trajectory Reconstruction of Drones using a Single Camera

Event cameras are sensors inspired by biological systems that specialize in
capturing changes in brightness. These emerging cameras offer many advantages
over conventional frame-based cameras, including high dynamic range, high frame
rates, and extremely low power consumption. Due to these advantages, event
cameras have increasingly been adapted in various fields, such as frame
interpolation, semantic segmentation, odometry, and SLAM. However, their
application in 3D reconstruction for VR applications is underexplored. Previous
methods in this field mainly focused on 3D reconstruction through depth map
estimation. Methods that produce dense 3D reconstruction generally require
multiple cameras, while methods that utilize a single event camera can only
produce a semi-dense result. Other single-camera methods that can produce dense
3D reconstruction rely on creating a pipeline that either incorporates the
aforementioned methods or other existing Structure from Motion (SfM) or
Multi-view Stereo (MVS) methods. In this paper, we propose a novel approach for
solving dense 3D reconstruction using only a single event camera. To the best
of our knowledge, our work is the first attempt in this regard. Our preliminary
results demonstrate that the proposed method can produce visually
distinguishable dense 3D reconstructions directly without requiring pipelines
like those used by existing methods. Additionally, we have created a synthetic
dataset with $39,739$ object scans using an event camera simulator. This
dataset will help accelerate other relevant research in this field.

使用事件相机，提出了一种解决仅使用单个事件相机进行密集三维重建的新方法，结果显示该方法无需使用现有方法的流水线即可直接生成可视化区分的密集三维重建，同时创建了一个使用事件相机模拟器生成的合成数据集，加速了相关研究。

使用单目事件相机进行密集体素三维重建

Dense Voxel 3D Reconstruction Using a Monocular Event Camera

Current state-of-the-art solutions for motion capture from a single camera
are optimization driven: they optimize the parameters of a 3D human model so
that its re-projection matches measurements in the video (e.g. person
segmentation, optical flow, keypoint detections etc.). Optimization models are
susceptible to local minima. This has been the bottleneck that forced using
clean green-screen like backgrounds at capture time, manual initialization, or
switching to multiple cameras as input resource. In this work, we propose a
learning based motion capture model for single camera input. Instead of
optimizing mesh and skeleton parameters directly, our model optimizes neural
network weights that predict 3D shape and skeleton configurations given a
monocular RGB video. Our model is trained using a combination of strong
supervision from synthetic data, and self-supervision from differentiable
rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c)
human-background segmentation, in an end-to-end framework. Empirically we show
our model combines the best of both worlds of supervised learning and test-time
optimization: supervised learning initializes the model parameters in the right
regime, ensuring good pose and surface initialization at test time, without
manual effort. Self-supervision by back-propagating through differentiable
rendering allows (unsupervised) adaptation of the model to the test data, and
offers much tighter fit than a pretrained fixed model. We show that the
proposed model improves with experience and converges to low-error solutions
where previous optimization methods fail.

本文提出了一种基于学习的单摄像头动作捕捉模型，通过神经网络权重优化预测 3D 形状和骨架配置，结合合成数据的强监督和可微渲染的自监督，实现了在测试时间自适应，并展示了比传统优化方法更好的效果。