title: MonST3R: A Simple Approach for Estimating Geometry in the Presence of
    Motion
summary: 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.
paper_authors._id: 6704c2324d2db34694c9cd2b
paper_authors.name: Junyi Zhang
paper_authors.status: claimed_verified
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paper_authors.fullname: Junyi Zhang
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paper_authors.user: Junyi42
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paper_authors.name: Charles Herrmann
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paper_authors.fullname: Charles Herrmann
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paper_authors.user: irwinherrmann
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paper_authors.name: Junhwa Hur
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paper_authors.fullname: Junhwa Hur
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paper_authors.user: hurjunhwa
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paper_authors.name: Varun Jampani
paper_authors._id: 6704c2324d2db34694c9cd2f
paper_authors.name: Trevor Darrell
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paper_authors._id: 64cbdf02f103036e23d1c7f3
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paper_authors.fullname: Trevor Darrell
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paper_authors.user: trevordarrell
paper_authors._id: 6704c2324d2db34694c9cd30
paper_authors.name: Forrester Cole
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-08T09:51:26.698Z
paper_authors._id: 635003011d81beb8e244e68d
paper_authors.avatarUrl: /avatars/32f46e6778d1c52ebdd6f2511ba48257.svg
paper_authors.fullname: Forrester Cole
paper_authors.type: user
paper_authors.user: fcole
paper_authors._id: 6704c2324d2db34694c9cd31
paper_authors.name: Deqing Sun
paper_authors._id: 6704c2324d2db34694c9cd32
paper_authors.name: Ming-Hsuan Yang
paper_title: MonST3R: A Simple Approach for Estimating Geometry in the Presence of
    Motion
paper_summary: 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.
paper_ai_summary: A novel geometry-first approach, Motion DUSt3R, directly estimates per-timestep geometry in dynamic scenes, demonstrating strong performance on tasks like video depth and camera pose estimation through fine-tuning on limited dynamic datasets.
paper_ai_keywords: dynamic scenes depth estimation optical flow fine-tuning 4D reconstruction