# Incremental Object Database: Building 3D Models from Multiple Partial Observations

arXiv: https://arxiv.org/abs/1808.00760 · HTML: https://mlsanity.com/paper/1808.00760/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 15 of 15)

- [SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences](https://mlsanity.com/paper/2103.14898/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [Volumetric Semantically Consistent 3D Panoptic Mapping](https://mlsanity.com/paper/2309.14737/) (2023)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction](https://mlsanity.com/paper/2105.07468/) (2021)
- [Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics](https://mlsanity.com/paper/2210.09814/) (2022)
- [OVI-MAP:Open-Vocabulary Instance-Semantic Mapping](https://mlsanity.com/paper/2603.26541/) (2026)
- [FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models](https://mlsanity.com/paper/2402.04555/) (2024)
- [Indoor Semantic Scene Understanding using Multi-modality Fusion](https://mlsanity.com/paper/2108.07616/) (2021)
- [Semantic Dense Reconstruction with Consistent Scene Segments](https://mlsanity.com/paper/2109.14821/) (2021)
- [ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification](https://mlsanity.com/paper/2311.02734/) (2023)
- [Semantic Object-level Modeling for Robust Visual Camera Relocalization](https://mlsanity.com/paper/2402.06951/) (2024)
- [NeurNCD: Novel Class Discovery via Implicit Neural Representation](https://mlsanity.com/paper/2506.06412/) (2025)

## Abstract (arXiv)

Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.