# SceneSuggest: Context-driven 3D Scene Design

arXiv: https://arxiv.org/abs/1703.00061 · HTML: https://mlsanity.com/paper/1703.00061/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2017.

## Built on by (most cited first, 8 of 8)

- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://mlsanity.com/paper/2303.14207/) (2023)
- [Learning 3D Scene Priors with 2D Supervision](https://mlsanity.com/paper/2211.14157/) (2022)
- [Language-driven Scene Synthesis using Multi-conditional Diffusion Model](https://mlsanity.com/paper/2310.15948/) (2023)
- [3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation](https://mlsanity.com/paper/2507.18625/) (2025)
- [Contextual Scene Augmentation and Synthesis via GSACNet](https://mlsanity.com/paper/2103.15369/) (2021)
- [Causal Reasoning Elicits Controllable 3D Scene Generation](https://mlsanity.com/paper/2509.15249/) (2025)
- [StyleForge: Indoor Furniture Styling by Counterfactual Reasoning in a Hypergraph Field](https://mlsanity.com/paper/2608.01954/) (2026)

## Abstract (arXiv)

We present SceneSuggest: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement. Using a point-and-click metaphor we specify regions in a scene in which to automatically place and orient relevant 3D models. Candidate models are ranked using a set of static support, position, and orientation priors learned from 3D scenes. We show that our suggestions enable rapid assembly of indoor scenes. We perform a user study comparing suggestions to manual search and selection, as well as to suggestions with no automatic orientation. We find that suggestions reduce total modeling time by 32%, that orientation priors reduce time spent re-orienting objects by 27%, and that context-driven suggestions reduce the number of text queries by 50%.