# Active Object Reconstruction Using a Guided View Planner

arXiv: https://arxiv.org/abs/1805.03081 · HTML: https://mlsanity.com/paper/1805.03081/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 5 of 5)

- [Uncertainty-Driven Active Vision for Implicit Scene Reconstruction](https://mlsanity.com/paper/2210.00978/) (2022)
- [Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image](https://mlsanity.com/paper/2210.05891/) (2022)
- [Interaction-Driven Active 3D Reconstruction with Object Interiors](https://mlsanity.com/paper/2310.14700/) (2023)
- [Explore Contextual Information for 3D Scene Graph Generation](https://mlsanity.com/paper/2210.06240/) (2022)
- [Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction](https://mlsanity.com/paper/2505.13091/) (2025)

## Abstract (arXiv)

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.