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20232026
most citedPoint-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

2 citations · 3 across the 6 of their papers we have counts for

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11 papers · 1 filter

cs.CV2026

UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction

Xinqiang Yu, Zekun qi, Jiawei He +6

Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized but object-aware, or part-awar…

cs.CV2025

DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge

Wenyao Zhang, Hongsi Liu, Zekun Qi +11

Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot ma…

cs.CV2025

DexVLG: Dexterous Vision-Language-Grasp Model at Scale

Jiawei He, Danshi Li, Xinqiang Yu +7

As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection,…

cs.CV20251 cited

OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models

Mengdi Jia, Zekun Qi, Shaochen Zhang +5

Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or impr…

cs.CV2024

Positional Prompt Tuning for Efficient 3D Representation Learning

Shaochen Zhang, Zekun Qi, Runpei Dong +2

We rethink the role of positional encoding in 3D representation learning and fine-tuning. We argue that using positional encoding in point Transformer-based methods serves to aggre…

cs.CV2024

DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation

Yuang Peng, Yuxin Cui, Haomiao Tang +7

Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive ability to creatively generate personalized content across var…