Publication
HI^2: Sparse-View 3D Object Reconstruction with a Hybrid Implicit Initialization
Pragati Jaiswal; Didier Stricker
In: Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2025), February 23-25, Porto, Portugal, SCITEPRESS (Science and Technology Publications, Lda), 2025.
Abstract
Accurate 3D object reconstruction is essential for various applications, including mixed reality and medicine. Recent advancements in deep learning-based methods and implicit 3D modelling have significantly enhanced the accuracy of 3D object reconstruction. Traditional methods enable reconstruction from a limited number of images, while implicit 3D modelling is proficient at capturing fine details and complex topologies. In this paper, we present a novel pipeline for 3D object reconstruction that combines the strengths of both approaches. Firstly, we use a 3D occupancy grid to generate a coarse 3D object from a few images. Secondly, we implement a novel and effective sampling strategy to transform the coarse reconstruction into an implicit representation, which is optimized to reduce computation power and training time. This sampling strategy also allows it to be true to scale given actual camera intrinsic and extrinsic parameters. Finally, we refine the implicit representation and extract the 3D object mesh under a differentiable rendering scheme. Experiments on several datasets demonstrate that our proposed approach can reconstruct accurate 3D objects and outperforms state-of-the-art methods in terms of the Chamfer distance and Peak Signal-to-Noise Ratio metrics.