# GIFT: A Real-time and Scalable 3D Shape Search Engine

arXiv: https://arxiv.org/abs/1604.01879 · HTML: https://mlsanity.com/paper/1604.01879/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2016.

## Built on by (most cited first, 30 of 31)

- [PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis](https://mlsanity.com/paper/2112.04903/) (2021)
- [MVT: Multi-view Vision Transformer for 3D Object Recognition](https://mlsanity.com/paper/2110.13083/) (2021)
- [RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning](https://mlsanity.com/paper/2202.13094/) (2022)
- [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://mlsanity.com/paper/2311.02608/) (2023)
- [Computation-efficient Deep Learning for Computer Vision: A Survey](https://mlsanity.com/paper/2308.13998/) (2023)
- [Interpreting Representation Quality of DNNs for 3D Point Cloud Processing](https://mlsanity.com/paper/2111.03549/) (2021)
- [What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives](https://mlsanity.com/paper/2211.06009/) (2022)
- [Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects](https://mlsanity.com/paper/2401.00405/) (2023)
- [MVTN: Learning Multi-View Transformations for 3D Understanding](https://mlsanity.com/paper/2212.13462/) (2022)
- [Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program](https://mlsanity.com/paper/2212.12952/) (2022)
- [Ensemble Quadratic Assignment Network for Graph Matching](https://mlsanity.com/paper/2403.06457/) (2024)
- [AttWalk: Attentive Cross-Walks for Deep Mesh Analysis](https://mlsanity.com/paper/2104.11571/) (2021)
- [MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding](https://mlsanity.com/paper/2402.10002/) (2024)
- [Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images](https://mlsanity.com/paper/2207.08925/) (2022)
- [Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding](https://mlsanity.com/paper/2312.16477/) (2023)
- [SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval](https://mlsanity.com/paper/2307.10601/) (2023)
- [Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding](https://mlsanity.com/paper/2501.14238/) (2025)
- [MVIP -- A Dataset and Methods for Application Oriented Multi-View and Multi-Modal Industrial Part Recognition](https://mlsanity.com/paper/2502.15448/) (2025)
- [OOWL500: Overcoming Dataset Collection Bias in the Wild](https://mlsanity.com/paper/2108.10992/) (2021)
- [R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition](https://mlsanity.com/paper/2211.11085/) (2022)
- [Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2407.01810/) (2024)
- [VSFormer: Mining Correlations in Flexible View Set for Multi-view 3D Shape Understanding](https://mlsanity.com/paper/2409.09254/) (2024)
- [ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs](https://mlsanity.com/paper/2103.11288/) (2021)
- [Robust Pooling through the Data Mode](https://mlsanity.com/paper/2106.10850/) (2021)
- [A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval](https://mlsanity.com/paper/2109.12299/) (2021)
- [PREMA: Part-based REcurrent Multi-view Aggregation Network for 3D Shape Retrieval](https://mlsanity.com/paper/2111.04945/) (2021)
- [ViewFormer: View Set Attention for Multi-view 3D Shape Understanding](https://mlsanity.com/paper/2305.00161/) (2023)
- [MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation](https://mlsanity.com/paper/2105.13277/) (2021)
- [Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point Cloud Feature Extractors](https://mlsanity.com/paper/2209.12770/) (2022)
- [Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy](https://mlsanity.com/paper/2505.18664/) (2025)

## Abstract (arXiv)

Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration; (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching; (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based re-ranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File Twice, as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.