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.
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.
PointLearner is a point cloud representation learning network inspired by biological vision. It uses a point-focused attention mechanism that mimics foveal vision by competitively weighting local neighbors against spatially downsampled features extracted via learnable inducing points. Additionally, a context-scan state space emulates eye saccades, inferring global scene structure through a Hilbert‑curve guided scan path, enabling robust performance on various point cloud tasks.
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.
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.
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.
arXiv:2609.23733v1 Announce Type: new Abstract: Feed-forward visual geometry models such as the Visual Geometry Grounded Transformer (VGGT) have recently enabled direct 3D reconstruction from multi-v...
arXiv:2610.27418v1 Announce Type: new Abstract: 3D point clouds generation requires capturing both part-level layout and surface detail while preserving permutation equivariance. Existing latent gene...
Zebra3D introduces a layerwise sparse‑linear hybrid attention framework that enhances the efficiency of pretrained 3D foundation models without requiring training from scratch. By assigning linear‑centered attention to tolerant early stages and tile‑sparse attention to sensitive middle‑to‑late stages, it preserves context through landmark summaries and enables training‑free conversion via closed‑form affine fitting. Across multiple backbones, Zebra3D achieves strong reconstruction and camera‑pose accuracy with high attention sparsity, delivering up to 7.1× speedups at 1,024 views and maintaining performance close to dense counterparts after optional post‑conversion training.
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...
arXiv:2604.19609v2 Announce Type: replace Abstract: Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong dom...
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.
The paper introduces the Register Anything Model (RAM), a unified approach for 3D point cloud registration that can robustly estimate 6‑DoF transformations across a wide range of domains, including indoor, outdoor, and object scenes. RAM overcomes domain‑specific challenges by employing a geometry‑aware normalization that voxelizes and standardizes point clouds based on their geometric properties, followed by a hierarchical coarse‑to‑fine pipeline with a geometric transformer that focuses on local consistency. Experiments on 12 benchmarks show that RAM achieves state‑of‑the‑art inlier ratios and registration recall, outperforming methods trained on individual domains and demonstrating strong generalization.