Dynamic point maps: a versatile representation for dynamic 3D reconstruction

Conference item

DUSt3R has recently demonstrated that many tasks in multiview geometry, including estimating camera intrinsics and extrinsics, reconstructing 3D scenes, and establishing image correspondences, can be reduced to predicting a pair of viewpoint-invariant point maps, i.e., pixel-aligned point clouds defined in a common reference frame. While this formulation is elegant and powerful, it is limited to static scenes. To overcome this limitation, we introduce the concept of Dynamic Point Maps (DPM), which extends standard point maps to support 4D tasks such as motion segmentation, scene flow estimation, 3D object tracking, and 2D correspondence. Our key insight is that, when time is introduced, several possible spatial and temporal references can be used to define the point maps. We identify a minimal subset of these combinations that can be regressed by a network to solve the aforementioned tasks. We train a DPM predictor on a mixture of synthetic and real data and evaluate it across diverse benchmarks, including video depth prediction, dynamic point cloud reconstruction, 3D scene flow, and object pose tracking, achieving stateof-the-art performance. Additional results are available at https://www.robots.ox.ac.uk/˜vgg/research/dynamic-pointmaps/.

Authors: Sucar, E; Lai, Z; Insafutdinov, E; Vedaldi, A

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: IEEE
• Acceptance date: 2025-07-23
• Event title: International Conference on Computer Vision (ICCV 2025)
• Event location: Honolulu, Hawai'i
• Event website: https://iccv.thecvf.com/
• Event start date: 2025-10-19
• Event end date: 2025-10-23
• Language: English