Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Method
3.1 Overview of Our Method
3.2 Coarse Text-cell Retrieval
3.3 Fine Position Estimation
3.4 Training Objectives


Experiments
4.1 Dataset Description and 4.2 Implementation Details
4.3 Evaluation Criteria and 4.4 Results


Performance Analysis
5.1 Ablation Study
5.2 Qualitative Analysis
5.3 Text Embedding Analysis


Conclusion and References Related Work Related Work Related Work Method
3.1 Overview of Our Method
3.2 Coarse Text-cell Retrieval
3.3 Fine Position Estimation
3.4 Training Objectives Method 3.1 Overview of Our Method 3.1 Overview of Our Method 3.2 Coarse Text-cell Retrieval 3.2 Coarse Text-cell Retrieval 3.3 Fine Position Estimation 3.3 Fine Position Estimation 3.4 Training Objectives 3.4 Training Objectives Experiments
4.1 Dataset Description and 4.2 Implementation Details
4.3 Evaluation Criteria and 4.4 Results Experiments 4.1 Dataset Description and 4.2 Implementation Details 4.1 Dataset Description and 4.2 Implementation Details 4.3 Evaluation Criteria and 4.4 Results 4.3 Evaluation Criteria and 4.4 Results Performance Analysis
5.1 Ablation Study
5.2 Qualitative Analysis
5.3 Text Embedding Analysis Performance Analysis 5.1 Ablation Study 5.1 Ablation Study 5.2 Qualitative Analysis 5.2 Qualitative Analysis 5.3 Text Embedding Analysis 5.3 Text Embedding Analysis Conclusion and References Conclusion and References Conclusion and References Supplementary Material Supplementary Material Details of KITTI360Pose Dataset
More Experiments on the Instance Query Extractor
Text-Cell Embedding Space Analysis
More Visualization Results
Point Cloud Robustness Analysis Details of KITTI360Pose Dataset Details of KITTI360Pose Dataset More Experiments on the Instance Query Extractor More Experiments on the Instance Query Extractor Text-Cell Embedding Space Analysis Text-Cell Embedding Space Analysis More Visualization Results More Visualization Results Point Cloud Robustness Analysis Point Cloud Robustness Analysis Anonymous Authors Anonymous Authors Details of KITTI360Pose Dataset
More Experiments on the Instance Query Extractor
Text-Cell Embedding Space Analysis
More Visualization Results
Point Cloud Robustness Analysis Details of KITTI360Pose Dataset Details of KITTI360Pose Dataset More Experiments on the Instance Query Extractor More Experiments on the Instance Query Extractor Text-Cell Embedding Space Analysis Text-Cell Embedding Space Analysis More Visualization Results More Visualization Results Point Cloud Robustness Analysis Point Cloud Robustness Analysis 5.3 Text Embedding Analysis Recent years have seen the emergence of large language models (encoders) like BERT [14], RoBERTa [24], T5 [33], and the CLIP [31] text encoder, each is trained with varied tasks and datasets. Text2Loc highlights that a pre-trained T5 model significantly enhances text and point cloud feature alignment. Yet, the potential of other models, such as RoBERTa and the CLIP text encoder, known for their excellence in visual grounding tasks, is not explored in their study. Thus, we conduct a comparative analysis of T5-small, RoBERTa-base, and the CLIP text encoder within our model framework. The result in Table 6 indicates that the T5-small (61M) achieves 0.24/0.46/0.57 at the top-1/3/5 recall metrics, incrementally outperforming RoBERTabase (125M) and CLIP text (123M) with fewer parameters. 6 CONCLUSION In this paper, we propose a IFRP-T2P model for 3D point cloud localization based on a few natural language descriptions, which is the first approach to directly take raw point clouds as input, eliminating the need for ground-truth instances. Additionally, we propose the RowColRPA in the coarse stage and the RPCA in the fine stage to fully leverage the spatial relation information. Through extensive experiments, IFRP-T2P achieves comparable performance with the state-of-the-art model, Text2Loc, which relies on groundtruth instances. Moreover, it surpasses the Text2Loc using instance segmentation model as prior. Our approach expands the usability of existing text-to-point-cloud localization models, enabling their application in scenarios where few instance information is available. Our future work will focus on applying IFRP-T2P for navigation in real-world robotic applications, bridging the gap between theoretical models and practical utility in autonomous navigation and interaction. REFERENCES [1] Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, and Leonidas Guibas. 2020. ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes. 16th European Conference on Computer Vision (ECCV) (2020). [2] Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. 2018. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition. 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(1) Lichao Wang, FNii, CUHKSZ (wanglichao1999@outlook.com);
(2) Zhihao Yuan, FNii and SSE, CUHKSZ (zhihaoyuan@link.cuhk.edu.cn);
(3) Jinke Ren, FNii and SSE, CUHKSZ (jinkeren@cuhk.edu.cn);
(4) Shuguang Cui, SSE and FNii, CUHKSZ (shuguangcui@cuhk.edu.cn);
(5) Zhen Li, a Corresponding Author from SSE and FNii, CUHKSZ (lizhen@cuhk.edu.cn). Authors: Authors: (1) Lichao Wang, FNii, CUHKSZ (wanglichao1999@outlook.com); (2) Zhihao Yuan, FNii and SSE, CUHKSZ (zhihaoyuan@link.cuhk.edu.cn); (3) Jinke Ren, FNii and SSE, CUHKSZ (jinkeren@cuhk.edu.cn); (4) Shuguang Cui, SSE and FNii, CUHKSZ (shuguangcui@cuhk.edu.cn); (5) Zhen Li, a Corresponding Author from SSE and FNii, CUHKSZ (lizhen@cuhk.edu.cn). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. available on arxiv available on arxiv

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