Fast Encoder-Based 3D from Casual Videos via Point Track Processing (2404.07097v2)

Abstract: This paper addresses the long-standing challenge of reconstructing 3D structures from videos with dynamic content. Current approaches to this problem were not designed to operate on casual videos recorded by standard cameras or require a long optimization time. Aiming to significantly improve the efficiency of previous approaches, we present TracksTo4D, a learning-based approach that enables inferring 3D structure and camera positions from dynamic content originating from casual videos using a single efficient feed-forward pass. To achieve this, we propose operating directly over 2D point tracks as input and designing an architecture tailored for processing 2D point tracks. Our proposed architecture is designed with two key principles in mind: (1) it takes into account the inherent symmetries present in the input point tracks data, and (2) it assumes that the movement patterns can be effectively represented using a low-rank approximation. TracksTo4D is trained in an unsupervised way on a dataset of casual videos utilizing only the 2D point tracks extracted from the videos, without any 3D supervision. Our experiments show that TracksTo4D can reconstruct a temporal point cloud and camera positions of the underlying video with accuracy comparable to state-of-the-art methods, while drastically reducing runtime by up to 95\%. We further show that TracksTo4D generalizes well to unseen videos of unseen semantic categories at inference time.

• The paper presents TracksTo4D, a novel deep learning approach that reconstructs non-rigid 3D structure and camera motion from casual videos using 2D point tracks.
• It utilizes an encoder-based network with equivariant learning to handle point permutations and temporal consistency, significantly reducing inference time.
• Training on in-the-wild videos without 3D ground truth, the method minimizes reprojection errors, demonstrating practical potential for real-world 3D reconstruction.

Learning Priors for Non-Rigid Structure from Motion from Casual Videos

Introduction to Non-Rigid Structure from Motion

In the field of 3D reconstruction, tackling the challenge of deducing 3D structures and camera positions from video sequences, particularly when the objects in view undergo non-rigid transformations, poses significant difficulties. Traditional methods for solving non-rigid Structure from Motion (SfM) often rely on unrealistic assumptions or are hampered by lengthy optimization times, limiting their applicability to real-world scenarios. This paper introduces a novel approach, TracksTo4D, designed to address these challenges by leveraging deep learning techniques and recent advancements in point tracking technology.

Deep Learning Approach: TracksTo4D

TracksTo4D offers a breakthrough in inferring 3D structure and camera positions from dynamically captured content within casual, in-the-wild videos. At its core, this method utilizes a deep neural network to process sparse point track matrices, extracted from video frames, in a single feed-forward pass. This process diverges from traditional methods that primarily focus on the pixel or semantic feature level, proposing instead a generic, class-agnostic feature learning approach directly from 2D point tracks.

The neural architecture of TracksTo4D is tailored to recognize and utilize the inherent symmetries in 2D point tracks, specifically employing equivariant learning principles to manage permutations of points and the temporal structure of video frames. This enables the system to generalize well across various unseen video categories by focusing on the motion patterns shared across different semantic categories.

Training and Implementation

TracksTo4D is trained on a dataset of in-the-wild videos without any 3D ground truth, relying solely on 2D point tracks extracted from these videos. This is accomplished by minimizing reprojection errors during training, thereby enabling the learning of 3D locations and camera motion implicitly. The experiments conducted demonstrate that TracksTo4D can generalize effectively to unseen videos, achieving comparable results to state-of-the-art methods while substantially reducing inference time.

Implications and Future Directions

The introduction of TracksTo4D has significant implications for the field of 3D reconstruction, particularly in scenarios involving non-rigid motion. By leveraging deep learning in conjunction with advancements in point tracking, this approach paves the way for more efficient and accurate 3D reconstruction methods that can be applied to a wide range of real-world scenarios.

Looking forward, the potential for further refining this technology is vast. Improvements in point tracking accuracy and speed could directly enhance the performance of TracksTo4D. Additionally, integrating priors from depth-from-single-image models as supplementary input could expand the system's capabilities, especially in scenarios with minimal motion parallax.

Conclusion

The development of TracksTo4D represents a significant step forward in the quest for efficient and accurate non-rigid Structure from Motion methods. By sidestepping the limitations of traditional approaches and harnessing the power of deep learning and point tracking technologies, it offers a promising avenue for future research and application in the domain of 3D reconstruction from casual videos.