ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos

Shi Chen, Erik Sandström, Sandro Lombardi, Siyuan Li, Martin R. Oswald

Keywords: Dynamic, scene reconstruction, dynamic scene reconstruction, 4D reconstruction, Gaussian Splatting, SLAM, monocular, dynamic SLAM, pose estimation, mapping, tracking

Abstract: Achieving truly practical dynamic 3D reconstruction requires online operation, global pose and map consistency, detailed appearance modeling, and the flexibility to handle both RGB and RGB-D inputs. However, existing SLAM methods typically merely remove the dynamic parts or require RGB-D input, while offline methods are not scalable to long video sequences, and current transformer-based feedforward methods lack global consistency and appearance details. To this end, we achieve online dynamic scene reconstruction by disentangling the static and dynamic parts within a SLAM system. The poses are tracked robustly with a novel motion masking strategy, and dynamic parts are reconstructed leveraging a progressive adaptation of a Motion Scaffolds graph. Our method yields novel view renderings competitive to offline methods and achieves on-par tracking with state-of-the-art dynamic SLAM methods.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 7413