D^2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic Scenes

• URL: http://arxiv.org/abs/2504.06264v1
• Date: Tue, 08 Apr 2025 17:59:50 GMT
• Title: D^2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic Scenes
• Authors: Jisang Han, Honggyu An, Jaewoo Jung, Takuya Narihira, Junyoung Seo, Kazumi Fukuda, Chaehyun Kim, Sunghwan Hong, Yuki Mitsufuji, Seungryong Kim,
• Abstract summary: We propose D2USt3R that regresses 4D pointmaps simuliously capture both static and dynamic 3D scene geometry in a feed-forward manner.<n>By explicitly incorporating both spatial and temporal aspects, our approach successfully encapsulates object-temporal dense correspondence to the proposed 4D pointmaps, enhancing downstream tasks.
• Score: 40.371542172080105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the task of 3D reconstruction in dynamic scenes, where object motions degrade the quality of previous 3D pointmap regression methods, such as DUSt3R, originally designed for static 3D scene reconstruction. Although these methods provide an elegant and powerful solution in static settings, they struggle in the presence of dynamic motions that disrupt alignment based solely on camera poses. To overcome this, we propose D^2USt3R that regresses 4D pointmaps that simultaneiously capture both static and dynamic 3D scene geometry in a feed-forward manner. By explicitly incorporating both spatial and temporal aspects, our approach successfully encapsulates spatio-temporal dense correspondence to the proposed 4D pointmaps, enhancing downstream tasks. Extensive experimental evaluations demonstrate that our proposed approach consistently achieves superior reconstruction performance across various datasets featuring complex motions.

Related papers

• St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World [106.91539872943864]
  St4RTrack is a framework that simultaneously reconstructs and tracks dynamic video content in a world coordinate frame from RGB inputs. We predict both pointmaps at the same moment, in the same world, capturing both static and dynamic scene geometry. We establish a new extensive benchmark for world-frame reconstruction and tracking, demonstrating the effectiveness and efficiency of our unified, data-driven framework.
  arXiv  Detail & Related papers  (2025-04-17T17:55:58Z)
• POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction [53.19968902152528]
  We present POMATO, a unified framework for dynamic 3D reconstruction by marrying pointmap matching with temporal motion.<n>Specifically, our method learns an explicit matching relationship by mapping RGB pixels from both dynamic and static regions across different views to 3D pointmaps.<n>We show the effectiveness of the proposed pointmap matching and temporal fusion paradigm by demonstrating the remarkable performance across multiple downstream tasks.
  arXiv  Detail & Related papers  (2025-04-08T05:33:13Z)
• UnIRe: Unsupervised Instance Decomposition for Dynamic Urban Scene Reconstruction [27.334884564978907]
  We propose UnIRe, a 3D Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances.<n>At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space.<n>Experiments show that our method outperforms existing methods in dynamic scene reconstruction while enabling accurate and flexible instance-level editing.
  arXiv  Detail & Related papers  (2025-04-01T13:15:58Z)
• Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving [116.10577967146762]
  We propose Driv3R, a framework that directly regresses per-frame point maps from multi-view image sequences.<n>We employ a 4D flow predictor to identify moving objects within the scene to direct our network focus more on reconstructing these dynamic regions.<n>Driv3R outperforms previous frameworks in 4D dynamic scene reconstruction, achieving 15x faster inference speed.
  arXiv  Detail & Related papers  (2024-12-09T18:58:03Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly 4D Reconstruction [15.588032729272536]
  Current 3DGS-based streaming methods treat the Gaussian primitives uniformly and constantly renew the densified Gaussians.<n>We propose a novel three-stage pipeline for iterative streamable 4D dynamic spatial reconstruction.<n>Our method achieves state-of-the-art performance in online 4D reconstruction, demonstrating the fastest on-the-fly training, superior representation quality, and real-time rendering capability.
  arXiv  Detail & Related papers  (2024-11-22T10:47:47Z)
• Shape of Motion: 4D Reconstruction from a Single Video [51.04575075620677]
  We introduce a method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion. We exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE3 motion bases. Our method achieves state-of-the-art performance for both long-range 3D/2D motion estimation and novel view synthesis on dynamic scenes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.