L4P: Towards Unified Low-Level 4D Vision Perception

TL;DR

L4P introduces a unified, feedforward architecture leveraging a pre-trained ViT video encoder to efficiently handle multiple low-level 4D perception tasks simultaneously, matching specialized methods in performance.

Contribution

The paper proposes a general-purpose, unified model for low-level 4D perception tasks that is competitive with specialized methods and efficient in multi-task processing.

Findings

Competitive with specialized methods on dense tasks like depth and optical flow

Effective on sparse tasks such as 2D/3D tracking

Solves multiple tasks simultaneously with comparable speed

Abstract

The spatio-temporal relationship between the pixels of a video carries critical information for low-level 4D perception tasks. A single model that reasons about it should be able to solve several such tasks well. Yet, most state-of-the-art methods rely on architectures specialized for the task at hand. We present L4P, a feedforward, general-purpose architecture that solves low-level 4D perception tasks in a unified framework. L4P leverages a pre-trained ViT-based video encoder and combines it with per-task heads that are lightweight and therefore do not require extensive training. Despite its general and feedforward formulation, our method is competitive with existing specialized methods on both dense tasks, such as depth or optical flow estimation, and sparse tasks, such as 2D/3D tracking. Moreover, it solves all tasks at once in a time comparable to that of single-task methods.