GAPrompt++ is a multi-granular geometry-aware prompting method designed to adapt pre-trained 3D vision models to downstream tasks efficiently. It introduces a Point Shift Prompter for multi-scale geometric feature extraction, a Keypoint Prompter for local geometric saliency, and a Prompt Propagation mechanism to embed these cues throughout the model hierarchy. Experiments demonstrate that GAPrompt++ outperforms other prompting-based PEFT methods and even surpasses full fine-tuning while using less than 2% trainable parameters, and the authors provide two new challenging benchmarks for future research.
By Zixiang Ai, Zhenyu Cui, Yufei Guo, Wenwen Qiang, Lei Chen, Jiwen Lu, Jiahuan Zhou
LitePT introduces a lighter yet stronger 3D point cloud backbone that strategically uses convolutional layers in early, high‑resolution stages and switches to attention in deeper, low‑resolution layers. By adding a novel, parameter‑free 3D positional encoding called PointROPE, LitePT preserves spatial layout while reducing redundancy. The resulting model has 3.6× fewer parameters, runs twice as fast, and uses half the memory of the state‑of‑the‑art Point Transformer V3, yet matches or surpasses its performance across multiple tasks and datasets.
By Yuanwen Yue, Damien Robert, Jianyuan Wang, Sunghwan Hong, Jan Dirk Wegner, Christian Rupprecht, Konrad Schindler
arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.
By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
MixPT is a point transformer that combines window and global attention blocks in an interleaved schedule, using a fixed serialization pattern for window blocks. It applies global attention to spatially pooled tokens for large scenes, feeding residual updates back to the original-resolution stream. Experiments on indoor and outdoor benchmarks show MixPT achieves competitive accuracy with lower latency than existing window- or global-attention baselines, and can scale by repeating the interleaved pattern.
By Minkyun Seo, Hyungtae Lim, Jaesik Park
The paper introduces BRICE, a Brain-inspired Recurrent Illusory Contour Encoder designed to improve point cloud completion. It leverages a recurrent neural architecture based on Predictive Coding to generate illusory contours, mimicking how the brain completes visual information. BRICE can be integrated into existing completion models and demonstrates performance gains on datasets such as MVP2K, PCN, and PlantPCom.
By Yunlei Wang, Dawei Li, Xue-song Tang
PointLAM introduces a new point-based 3D object detection architecture that addresses efficiency and fidelity trade-offs inherent in LiDAR point cloud processing. It employs a Laplacian Point Sampler (LPS) to accelerate downsampling while preserving foreground structure, and a Local Hadamard Aggregator (LHA) that replaces costly continuous interactions with a topology‑aware gating mechanism. Combined with Bi‑Directional Mamba layers, the resulting Local Attentive Mamba (LAM) block delivers competitive performance on nuScenes and Waymo datasets, outperforming voxel‑based competitors in detecting small objects and handling extreme sparsity with a smaller computational footprint.
By Xuanming Shang, Weijia Zhang, Chao Ma
BlazeVGGT is a training‑free, plug‑and‑play module that speeds up the VGGT visual geometry model by sparsifying cross‑view attention. It selects a compact set of complementary views using a greedy determinantal point process, preserving dense per‑frame predictions while eliminating redundant computations. On 2,000‑frame sequences, BlazeVGGT achieves roughly 13× faster inference and improves Chamfer Distance from 0.41 to 0.32, demonstrating a strong accuracy–efficiency trade‑off for point‑cloud reconstruction and camera‑pose estimation.
By Shujun Yao, Junhui Lin, Congying Han, Tiande Guo, Ziwen Liu, Bonan Li
arXiv:2609.13318v1 Announce Type: cross
Abstract: 3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tig...
By Changbo Yan, Zhongbo Zhang, Zaibin Zhang, Yifan Wang, Lijun Wang, Huchuan Lu
Mainstream point cloud completion methods are unanimously based on deep learning architectures. On one hand, these methods tend to repetitively stacking attention modules to enhance network performanc...
MixPT is a point cloud transformer that interleaves window and global attention blocks while keeping a fixed window serialization pattern. It applies global attention to pooled tokens for large scenes, feeding residual updates back to the full-resolution stream. Experiments on indoor and outdoor benchmarks show MixPT matches or exceeds the accuracy of existing window or global attention models while achieving lower latency, and it can be scaled by repeating the interleaved pattern.
arXiv:2610.28120v1 Announce Type: new
Abstract: Feedforward reconstruction of dynamic driving scenes requires integrating observations across multiple views and time steps into a coherent 3D represen...
By Xianqi Wang, Cheng Chi, Yuechuan Pu, Hongcheng Luo, Junda Cheng, Guang Chen, Hangjun Ye, Bing Wang, Xin Yang
arXiv:2607. 26645v1 Announce Type: cross Abstract: Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion.
By Wenzhe He, Meng Wang, JiaWei Qian, Jinfeng Xu, Ying Liu, Ruihui Li