MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion
Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, Deqing Sun, Ming-Hsuan Yang
2024-10-08
Summary
This paper presents MonST3R, a new method for estimating the shapes and structures of moving objects in videos, which helps improve how we understand dynamic scenes in computer vision.
What's the problem?
Estimating the geometry of scenes where objects move and change over time is a big challenge in computer vision. Current methods often break down the problem into smaller tasks, which can lead to complex systems that are prone to errors. This makes it hard to accurately capture how objects move and interact in real life.
What's the solution?
To tackle this issue, the authors developed MonST3R, which directly estimates the geometry of dynamic scenes by creating a pointmap for each moment in time. They adapted an existing method (DUST3R) that worked well for still images to handle moving scenes. The researchers faced a challenge due to the lack of suitable training data, but they found ways to fine-tune their model using limited datasets. This allowed MonST3R to effectively estimate shapes and positions of moving objects without needing detailed motion information.
Why it matters?
This research is important because it offers a simpler and more effective way to analyze dynamic scenes in videos. By improving how we estimate geometry in moving environments, MonST3R can enhance applications like video analysis, robotics, and augmented reality, making these technologies more reliable and efficient.
Abstract
Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. Our key insight is that by simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. However, this approach presents a significant challenge: the scarcity of suitable training data, namely dynamic, posed videos with depth labels. Despite this, we show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics, even without an explicit motion representation. Based on this, we introduce new optimizations for several downstream video-specific tasks and demonstrate strong performance on video depth and camera pose estimation, outperforming prior work in terms of robustness and efficiency. Moreover, MonST3R shows promising results for primarily feed-forward 4D reconstruction.