# Triplet-Center Loss for Multi-View 3D Object Retrieval

arXiv: https://arxiv.org/abs/1803.06189 · HTML: https://mlsanity.com/paper/1803.06189/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 30 of 69)

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- [Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.01344/) (2021)
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- [MVTN: Learning Multi-View Transformations for 3D Understanding](https://mlsanity.com/paper/2212.13462/) (2022)
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- [PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification](https://mlsanity.com/paper/2207.04793/) (2022)
- [CORSAIR: Convolutional Object Retrieval and Symmetry-AIded Registration](https://mlsanity.com/paper/2103.06911/) (2021)
- [AttWalk: Attentive Cross-Walks for Deep Mesh Analysis](https://mlsanity.com/paper/2104.11571/) (2021)
- [Fine-Grained VR Sketching: Dataset and Insights](https://mlsanity.com/paper/2209.10008/) (2022)
- [Transparent Anomaly Detection via Concept-based Explanations](https://mlsanity.com/paper/2310.10702/) (2023)
- [Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrieval](https://mlsanity.com/paper/2507.21489/) (2025)
- [LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition](https://mlsanity.com/paper/2109.01291/) (2021)
- [Towards 3D VR-Sketch to 3D Shape Retrieval](https://mlsanity.com/paper/2209.10020/) (2022)

## Abstract (arXiv)

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected. In the paper, we study variants of deep metric learning losses for 3D object retrieval, which did not receive enough attention from this area. First , two kinds of representative losses, triplet loss and center loss, are introduced which could learn more discriminative features than traditional classification loss. Then, we propose a novel loss named triplet-center loss, which can further enhance the discriminative power of the features. The proposed triplet-center loss learns a center for each class and requires that the distances between samples and centers from the same class are closer than those from different classes. Extensive experimental results on two popular 3D object retrieval benchmarks and two widely-adopted sketch-based 3D shape retrieval benchmarks consistently demonstrate the effectiveness of our proposed loss, and significant improvements have been achieved compared with the state-of-the-arts.