# Object-Aware Guidance for Autonomous Scene Reconstruction

arXiv: https://arxiv.org/abs/1805.07794 · HTML: https://mlsanity.com/paper/1805.07794/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2018.

## Built on by (most cited first, 9 of 9)

- [RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream](https://mlsanity.com/paper/2104.14837/) (2021)
- [RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting](https://mlsanity.com/paper/2404.19706/) (2024)
- [XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis](https://mlsanity.com/paper/2406.18360/) (2024)
- [Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao](https://mlsanity.com/paper/2206.12216/) (2022)
- [DisARM: Displacement Aware Relation Module for 3D Detection](https://mlsanity.com/paper/2203.01152/) (2022)
- [ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion](https://mlsanity.com/paper/2105.05600/) (2021)
- [Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance](https://mlsanity.com/paper/2412.02249/) (2024)
- [Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction](https://mlsanity.com/paper/2508.01014/) (2025)
- [Glass Surface Detection Grounded in 3D Visual Geometry](https://mlsanity.com/paper/2608.26752/) (2026)

## Abstract (arXiv)

To carry out autonomous 3D scanning and online reconstruction of unknown indoor scenes, one has to find a balance between global exploration of the entire scene and local scanning of the objects within it. In this work, we propose a novel approach, which provides object-aware guidance for autoscanning, for exploring, reconstructing, and understanding an unknown scene within one navigation pass. Our approach interleaves between object analysis to identify the next best object (NBO) for global exploration, and object-aware information gain analysis to plan the next best view (NBV) for local scanning. First, an objectness-based segmentation method is introduced to extract semantic objects from the current scene surface via a multi-class graph cuts minimization. Then, an object of interest (OOI) is identified as the NBO which the robot aims to visit and scan. The robot then conducts fine scanning on the OOI with views determined by the NBV strategy. When the OOI is recognized as a full object, it can be replaced by its most similar 3D model in a shape database. The algorithm iterates until all of the objects are recognized and reconstructed in the scene. Various experiments and comparisons have shown the feasibility of our proposed approach.