Dynamic Scene Novel View Synthesis via Deferred Spatio-temporal Consistency

• URL: http://arxiv.org/abs/2109.01018v1
• Date: Thu, 2 Sep 2021 15:29:45 GMT
• Title: Dynamic Scene Novel View Synthesis via Deferred Spatio-temporal Consistency
• Authors: Beatrix-Em\H{o}ke F\"ul\"op-Balogh and Eleanor Tursman and James Tompkin and Julie Digne and Nicolas Bonneel
• Abstract summary: Structures (SfM) and novel view synthesis (NVS) are presented. SfM produces noisy-temporally reconstructed sparse clouds, resulting in NVS with temporally inconsistent effects. We demonstrate our algorithm on real-world dynamic scenes against classic more recent learning-based baseline approaches.
• Score: 18.036582072609882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint. Both are hard with casual capture and dynamic scenes: SfM produces noisy and spatio-temporally sparse reconstructed point clouds, resulting in NVS with spatio-temporally inconsistent effects. We consider SfM and NVS parts together to ease the challenge. First, for SfM, we recover stable camera poses, then we defer the requirement for temporally-consistent points across the scene and reconstruct only a sparse point cloud per timestep that is noisy in space-time. Second, for NVS, we present a variational diffusion formulation on depths and colors that lets us robustly cope with the noise by enforcing spatio-temporal consistency via per-pixel reprojection weights derived from the input views. Together, this deferred approach generates novel views for dynamic scenes without requiring challenging spatio-temporally consistent reconstructions nor training complex models on large datasets. We demonstrate our algorithm on real-world dynamic scenes against classic and more recent learning-based baseline approaches.

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