TracksTo4D Enables Efficient 3D Reconstruction from Videos

3D Reconstruction from Videos: TracksTo4D Enables Efficient Processing

The reconstruction of three-dimensional structures from two-dimensional videos, especially those with dynamic content, has long been a challenge in computer vision. Existing approaches often require long optimization times or are not designed for processing everyday videos captured with standard cameras. A new approach called TracksTo4D now promises to significantly accelerate this process.

TracksTo4D is based on a learning method and enables the derivation of 3D structures and camera positions from dynamic content in videos using a single, efficient feed-forward pass. In contrast to conventional methods, which often require complex iterative optimizations, TracksTo4D works directly with 2D point tracks as input and uses a specially designed architecture.

The design of this architecture follows two central principles. First, it considers the inherent symmetries of the input data of the point tracks. Second, it is based on the assumption that motion patterns can be effectively represented by a low-rank approximation. These two principles contribute to the efficiency and accuracy of the procedure.

The training of TracksTo4D is performed unsupervised on a dataset of everyday videos. Only the 2D point tracks extracted from the videos are used, without the need for 3D supervision. This approach allows for efficient training and good generalization to unseen videos.

Experimental results show that TracksTo4D is able to reconstruct a temporal point cloud and camera positions of the underlying video with an accuracy comparable to state-of-the-art methods. At the same time, TracksTo4D drastically reduces the runtime – by up to 95%. Furthermore, the method is shown to generalize well to unseen videos of unknown semantic categories.

The ability to efficiently reconstruct 3D structures from everyday videos opens up a variety of applications in various fields. From the creation of 3D models from private videos to the automated analysis of surveillance footage, TracksTo4D could make an important contribution to the advancement of 3D reconstruction technology.

Potential Applications

* Film and Television: Creation of realistic 3D models from video footage. * Robotics: Navigation and object recognition in dynamic environments. * Medicine: Analysis of motion sequences and creation of 3D models of organs. * Virtual Reality: Creation of immersive virtual environments from real videos. Bibliography: Kasten, Y., Lu, W., & Maron, H. (2024). Fast Encoder-Based 3D from Casual Videos via Point Track Processing. *arXiv*. https://arxiv.org/abs/2404.07097 Tracks-to-4d. (n.d.). https://tracks-to-4d.github.io/ Kasten, Y., Lu, W., & Maron, H. (2024). Fast Encoder-Based 3D from Casual Videos via Point Track Processing. *arXiv*. https://arxiv.org/html/2404.07097v2 OpenReview. (n.d.). https://openreview.net/forum?id=bqGAheAeQY¬eId=XnNoKZByPL NVIDIA Research. (2024). Fast Encoder-Based 3D Casual Videos Point Track Processing. https://research.nvidia.com/publication/2024-12_fast-encoder-based-3d-casual-videos-point-track-processing OpenReview. (n.d.). https://openreview.net/pdf/7435bce51ab20b66987bc7d838d163df90105dda.pdf Semantic Scholar. (n.d.). Learning Priors for Non-Rigid SfM from Casual Videos. https://www.semanticscholar.org/paper/Learning-Priors-for-Non-Rigid-SfM-from-Casual-Kasten-Lu/0fd584921e6a405af71277811f4a8c825b784c3c AlphaXiv. (2024). Fast Encoder-Based 3D from Casual Videos via Point Track Processing. https://www.alphaxiv.org/abs/2404.07097 Kasten, Y. (n.d.). https://ykasten.github.io/ NeurIPS. (2024). https://nips.cc/virtual/2024/papers.html