UVRM: A Scalable 3D Reconstruction Model from Unposed Videos

• URL: http://arxiv.org/abs/2501.09347v1
• Date: Thu, 16 Jan 2025 08:00:17 GMT
• Title: UVRM: A Scalable 3D Reconstruction Model from Unposed Videos
• Authors: Shiu-hong Kao, Xiao Li, Jinglu Wang, Chi-Keung Tang, Yu-Wing Tai, Yan Lu,
• Abstract summary: Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples. We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose.
• Score: 69.89526627921612
• License:
• Abstract: Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples, a process that is both time-consuming and prone to errors. Consequently, 3D reconstruction training has been confined to either synthetic 3D datasets or small-scale datasets with annotated poses. In this study, we investigate the feasibility of 3D reconstruction using unposed video data of various objects. We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose. UVRM uses a transformer network to implicitly aggregate video frames into a pose-invariant latent feature space, which is then decoded into a tri-plane 3D representation. To obviate the need for ground-truth pose annotations during training, UVRM employs a combination of the score distillation sampling (SDS) method and an analysis-by-synthesis approach, progressively synthesizing pseudo novel-views using a pre-trained diffusion model. We qualitatively and quantitatively evaluate UVRM's performance on the G-Objaverse and CO3D datasets without relying on pose information. Extensive experiments show that UVRM is capable of effectively and efficiently reconstructing a wide range of 3D objects from unposed videos.

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