Summary of Pointcloud-text Matching: Benchmark Datasets and a Baseline, by Yanglin Feng et al.
PointCloud-Text Matching: Benchmark Datasets and a Baseline
by Yanglin Feng, Yang Qin, Dezhong Peng, Hongyuan Zhu, Xi Peng, Peng Hu
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel instance-level retrieval task called PointCloud-Text Matching (PTM), which aims to match point-cloud queries with text queries in various scenarios, such as indoor localization and scene retrieval. The authors construct three new PTM benchmark datasets: 3D2T-SR, 3D2T-NR, and 3D2T-QA. Existing cross-modal matching methods are ineffective for PTM due to the challenges of noisy correspondence, sparsity, noise, or disorder of point clouds, and ambiguity, vagueness, or incompleteness of texts. To tackle these challenges, the authors propose a Robust PointCloud-Text Matching method (RoMa), consisting of two modules: Dual Attention Perception (DAP) and Robust Negative Contrastive Learning (RNCL). DAP leverages token-level and feature-level attention to reduce the adverse impact of noise and ambiguity, while RNCL handles noisy correspondence by dividing negative pairs into clean and noisy subsets. The authors demonstrate the superiority of RoMa through extensive experiments on their benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find a specific picture in a huge library based on a description or a sketch. This is what this paper is about – finding the exact match between a 3D point cloud (like a digital map) and text information. The authors create new datasets for this task, which could be used in applications like indoor navigation or scene retrieval. However, there are challenges in making these systems work well, such as noisy data or unclear descriptions. To solve these problems, the authors develop a new method called RoMa that uses attention mechanisms to focus on important features and handle noisy data. They test this method on their datasets and show it works better than other approaches. |
Keywords
» Artificial intelligence » Attention » Token
