Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking

• URL: http://arxiv.org/abs/2506.05763v1
• Date: Fri, 06 Jun 2025 05:42:05 GMT
• Title: Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking
• Authors: Puntawat Ponglertnapakorn, Supasorn Suwajanakorn,
• Abstract summary: We present a method for 3D ball trajectory estimation from a 2D tracking sequence.<n>Our method achieves state-of-the-art performance despite training solely on simulated data.<n>Our method can generalize to real-world scenarios with multiple trajectories, opening up a range of applications in sport analysis and virtual replay.
• Score: 10.237629959021875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method for 3D ball trajectory estimation from a 2D tracking sequence. To overcome the ambiguity in 3D from 2D estimation, we design an LSTM-based pipeline that utilizes a novel canonical 3D representation that is independent of the camera's location to handle arbitrary views and a series of intermediate representations that encourage crucial invariance and reprojection consistency. We evaluated our method on four synthetic and three real datasets and conducted extensive ablation studies on our design choices. Despite training solely on simulated data, our method achieves state-of-the-art performance and can generalize to real-world scenarios with multiple trajectories, opening up a range of applications in sport analysis and virtual replay. Please visit our page: https://where-is-the-ball.github.io.

Related papers

• TAPVid-3D: A Benchmark for Tracking Any Point in 3D [63.060421798990845]
  We introduce a new benchmark, TAPVid-3D, for evaluating the task of Tracking Any Point in 3D. This benchmark will serve as a guidepost to improve our ability to understand precise 3D motion and surface deformation from monocular video.
  arXiv  Detail & Related papers  (2024-07-08T13:28:47Z)
• Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling [14.341099905684844]
  This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space lose valuable signal information during latent encoding. Instead, we simply repeat and the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin U
  arXiv  Detail & Related papers  (2024-06-26T15:18:20Z)
• SpatialTracker: Tracking Any 2D Pixels in 3D Space [71.58016288648447]
  We propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth estimators. Tracking in 3D allows us to leverage as-rigid-as-possible (ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts.
  arXiv  Detail & Related papers  (2024-04-05T17:59:25Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [111.16358607889609]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation.<n>For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• 3D Visual Tracking Framework with Deep Learning for Asteroid Exploration [22.808962211830675]
  In this paper, we focus on the studied accurate and real-time method for 3D tracking. A new large-scale 3D asteroid tracking dataset is presented, including binocular video sequences, depth maps, and point clouds of diverse asteroids. We propose a deep-learning based 3D tracking framework, named as Track3D, which involves 2D monocular tracker and a novel light-weight amodal axis-aligned bounding-box network, A3BoxNet.
  arXiv  Detail & Related papers  (2021-11-21T04:14:45Z)
• Differentiable Event Stream Simulator for Non-Rigid 3D Tracking [82.56690776283428]
  Our differentiable simulator enables non-rigid 3D tracking of deformable objects from event streams. We show the effectiveness of our approach for various types of non-rigid objects and compare to existing methods for non-rigid 3D tracking.
  arXiv  Detail & Related papers  (2021-04-30T17:58:07Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping [23.456046776979903]
  We propose to leverage multiview data of textitstatic points in arbitrary scenes (static or dynamic) to learn a neural 3D mapping module. The neural 3D mapper consumes RGB-D data as input, and produces a 3D voxel grid of deep features as output. We show that our unsupervised 3D object trackers outperform prior unsupervised 2D and 2.5D trackers, and approach the accuracy of supervised trackers.
  arXiv  Detail & Related papers  (2020-08-04T02:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.