arXiv AI
Uncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation
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arXiv Machine Learning
1d ago
Role-based Effectiveness of an Epistemic Uncertainty Estimate in Training Models for Semantic Segmentation of 3D LiDAR Point Clouds
The paper studies how epistemic uncertainty estimates can be used in different ways—either as an additional input feature or as a per‑sample confidence weight in the loss function—to improve 3D LiDAR point cloud segmentation. Experiments on four large‑scale LiDAR benchmarks (ISPRS Vaihingen, AHN3, Paris‑Lille‑3D, and Toronto‑3D) show that uncertainty‑aware models outperform a baseline ANN in mean Intersection over Union, recover minority classes, and better align prediction error with estimated uncertainty. The results suggest that uncertainty can serve multiple roles in the segmentation pipeline beyond merely boosting accuracy.
arXiv Computer Vision
Sep 18
GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model
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.
arXiv AI
Jul 1
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
arXiv Computer Vision
Sep 18
SenseFuse: Label-Free Fusion of Image and Shape Encoders for Open-Vocabulary 3D Instance Segmentation
SenseFuse introduces a label‑free fusion approach that balances 2D image and 3D shape encoders for open‑vocabulary 3D instance segmentation. By selecting a scene‑level fusion weight through an adaptive, sensitivity‑based mechanism, it improves mask labeling accuracy across multiple datasets, recovering up to 93% of the potential gain from an oracle weight. The method demonstrates that image and shape encoders have complementary failure patterns, leading to higher instance AP in most evaluated settings.
arXiv Computer Vision
Sep 22
GAPS: Generative Active Pseudo-view Selection for Sparse-View 3D Gaussian Splatting
arXiv:2609.23436v1 Announce Type: new Abstract: Novel view synthesis from sparse observations is severely under-constrained. Although 3D Gaussian Splatting (3DGS) enables real-time rendering, it prod...
arXiv Computer Vision
Aug 24
Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...