3D Reconstruction of non-visible surfaces of objects from a Single Depth View -- Comparative Study
Not on the map. Why?
Abstract (arXiv)
Scene and object reconstruction is an important problem in robotics, in particular in planning collision-free trajectories or in object manipulation. This paper compares two strategies for the reconstruction of nonvisible parts of the object surface from a single RGB-D camera view. The first method, named DeepSDF predicts the Signed Distance Transform to the object surface for a given point in 3D space. The second method, named MirrorNet reconstructs the occluded objects' parts by generating images from the other side of the observed object. Experiments performed with objects from the ShapeNet dataset, show that the view-dependent MirrorNet is faster and has smaller reconstruction errors in most categories.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
6 of 8 references.
References · 6
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
- 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
- 2020CoReNet: Coherent 3D scene reconstruction from a single RGB imagecited by 45
- 2019Learning better generative models for dexterous, single-view grasping of novel objectscited by 4
- 2021MongeNet: Efficient Sampler for Geometric Deep Learningcited by 2
Built on by
Not cited yet.