R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras

• URL: http://arxiv.org/abs/2308.14713v1
• Date: Mon, 28 Aug 2023 17:13:49 GMT
• Title: R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras
• Authors: Aron Schmied, Tobias Fischer, Martin Danelljan, Marc Pollefeys, Fisher Yu
• Abstract summary: R3D3 is a multi-camera system for dense 3D reconstruction and ego-motion estimation. Our approach exploits spatial-temporal information from multiple cameras, and monocular depth refinement. We show that this design enables a dense, consistent 3D reconstruction of challenging, dynamic outdoor environments.
• Score: 106.52409577316389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense 3D reconstruction and ego-motion estimation are key challenges in autonomous driving and robotics. Compared to the complex, multi-modal systems deployed today, multi-camera systems provide a simpler, low-cost alternative. However, camera-based 3D reconstruction of complex dynamic scenes has proven extremely difficult, as existing solutions often produce incomplete or incoherent results. We propose R3D3, a multi-camera system for dense 3D reconstruction and ego-motion estimation. Our approach iterates between geometric estimation that exploits spatial-temporal information from multiple cameras, and monocular depth refinement. We integrate multi-camera feature correlation and dense bundle adjustment operators that yield robust geometric depth and pose estimates. To improve reconstruction where geometric depth is unreliable, e.g. for moving objects or low-textured regions, we introduce learnable scene priors via a depth refinement network. We show that this design enables a dense, consistent 3D reconstruction of challenging, dynamic outdoor environments. Consequently, we achieve state-of-the-art dense depth prediction on the DDAD and NuScenes benchmarks.

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