物理的プライアで強化したイベントベース3D再構成
Physical Priors Augmented Event-Based 3D Reconstruction
イベントカメラのデータに運動・幾何・密度の物理的プライアを課すことでNeRFを効率的に学習し、3D再構成を高精度化する手法を提案し、大規模データセットも構築した。
著者: Jiaxu Wang, Junhao He, Ziyi Zhang, Renjing Xu
分類: cs.RO
原文アブストラクト
3D neural implicit representations play a significant component in many robotic applications. However, reconstructing neural radiance fields (NeRF) from realistic event data remains a challenge due to the sparsities and the lack of information when only event streams are available. In this paper, we utilize motion, geometry, and density priors behind event data to impose strong physical constraints to augment NeRF training. The proposed novel pipeline can directly benefit from those priors to reconstruct 3D scenes without additional inputs. Moreover, we present a novel density-guided patch-based sampling strategy for robust and efficient learning, which not only accelerates training procedures but also conduces to expressions of local geometries. More importantly, we establish the first large dataset for event-based 3D reconstruction, which contains 101 objects with various materials and geometries, along with the groundtruth of images and depth maps for all camera viewpoints, which significantly facilitates other research in the related fields. The code and dataset will be publicly available at https://github.com/Mercerai/PAEv3d.
