# TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding

arXiv: https://arxiv.org/abs/2502.10392 · HTML: https://mlsanity.com/paper/2502.10392/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 30 of 31)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](https://mlsanity.com/paper/1912.08830/) (2019)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [Group-Free 3D Object Detection via Transformers](https://mlsanity.com/paper/2104.00678/) (2021)
- [3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment](https://mlsanity.com/paper/2308.04352/) (2023)
- [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022)
- [InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring](https://mlsanity.com/paper/2103.01128/) (2021)
- [Multi-View Transformer for 3D Visual Grounding](https://mlsanity.com/paper/2204.02174/) (2022)
- [Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds](https://mlsanity.com/paper/2112.08879/) (2021)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [SAT: 2D Semantics Assisted Training for 3D Visual Grounding](https://mlsanity.com/paper/2105.11450/) (2021)
- [3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection](https://mlsanity.com/paper/2204.06272/) (2022)
- [EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding](https://mlsanity.com/paper/2209.14941/) (2022)
- [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://mlsanity.com/paper/2112.00322/) (2021)
- [LanguageRefer: Spatial-Language Model for 3D Visual Grounding](https://mlsanity.com/paper/2107.03438/) (2021)
- [TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding](https://mlsanity.com/paper/2108.02388/) (2021)
- [Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud](https://mlsanity.com/paper/2103.16381/) (2021)
- [Generative Sparse Detection Networks for 3D Single-shot Object Detection](https://mlsanity.com/paper/2006.12356/) (2020)
- [TR3D: Towards Real-Time Indoor 3D Object Detection](https://mlsanity.com/paper/2302.02858/) (2023)
- [A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions](https://mlsanity.com/paper/2406.05785/) (2024)
- [Multi-branch Collaborative Learning Network for 3D Visual Grounding](https://mlsanity.com/paper/2407.05363/) (2024)

## Built on by (most cited first, 17 of 17)

- [SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding](https://mlsanity.com/paper/2506.21924/) (2025)
- [S2-MLLM: Boosting Spatial Reasoning Capability of MLLMs for 3D Visual Grounding with Structural Guidance](https://mlsanity.com/paper/2512.01223/) (2025)
- [Optimal Brain Connection: Towards Efficient Structural Pruning](https://mlsanity.com/paper/2508.05521/) (2025)
- [PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding](https://mlsanity.com/paper/2512.20907/) (2025)
- [FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding](https://mlsanity.com/paper/2506.13629/) (2025)
- [Investigating Domain Gaps for Indoor 3D Object Detection](https://mlsanity.com/paper/2508.17439/) (2025)
- [T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models](https://mlsanity.com/paper/2506.19498/) (2025)
- [ChangingGrounding: 3D Visual Grounding in Changing Scenes](https://mlsanity.com/paper/2510.14965/) (2025)
- [3D-DRES: Detailed 3D Referring Expression Segmentation](https://mlsanity.com/paper/2603.02896/) (2026)
- [UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing](https://mlsanity.com/paper/2603.08131/) (2026)
- [Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models](https://mlsanity.com/paper/2603.24721/) (2026)
- [OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding](https://mlsanity.com/paper/2512.23020/) (2025)
- [PC-CrossDiff: Point-Cluster Dual-Level Cross-Modal Differential Attention for Unified 3D Referring and Segmentation](https://mlsanity.com/paper/2603.17753/) (2026)
- [Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding](https://mlsanity.com/paper/2604.26261/) (2026)
- [CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models](https://mlsanity.com/paper/2606.07529/) (2026)
- [PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding](https://mlsanity.com/paper/2606.31148/) (2026)
- [Talk2Sensors: 3D Visual Grounding in Autonomous Driving via Sensor-Adaptive Physical Cue Matching](https://mlsanity.com/paper/2608.04568/) (2026)

## Abstract (arXiv)

In this paper, we propose an efficient multi-level convolution architecture for 3D visual grounding. Conventional methods are difficult to meet the requirements of real-time inference due to the two-stage or point-based architecture. Inspired by the success of multi-level fully sparse convolutional architecture in 3D object detection, we aim to build a new 3D visual grounding framework following this technical route. However, as in 3D visual grounding task the 3D scene representation should be deeply interacted with text features, sparse convolution-based architecture is inefficient for this interaction due to the large amount of voxel features. To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion. Specifically, TGP iteratively sparsifies the 3D scene representation and thus efficiently interacts the voxel features with text features by cross-attention. To mitigate the affect of pruning on delicate geometric information, CBA adaptively fixes the over-pruned region by voxel completion with negligible computational overhead. Compared with previous single-stage methods, our method achieves top inference speed and surpasses previous fastest method by 100% FPS. Our method also achieves state-of-the-art accuracy even compared with two-stage methods, with +1.13 lead of Acc@0.5 on ScanRefer, and +2.6 and +3.2 leads on NR3D and SR3D respectively. The code is available at hrefhttps://github.com/GWxuan/TSP3Dhttps://github.com/GWxuan/TSP3D.