8 papers
Robust Multi-Model Fitting through Learning Neighbor Regions
Chang Nie, Guangming Wang, Zhe Liu +1
Multi-model fitting involves fitting multiple models accurately in a noisy environment. It is the basis for computer vision tasks such as scene reconstruction and mixed reality. Ho…
DiffSAC: Diffusion-guided Sampling for Consensus-based Robust Estimation
Chang Nie, Guangming Wang, Zhe Liu +1
Robust estimation is a core computer vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampling as they struggle to identi…
RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
Tianchen Deng, Zhiheng Feng, Wenhua Wu +4
Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based…
Teach and Grow: An Agent-Centered Architecture for General Robot Learning
Chang Nie, Zhe Liu, Hesheng Wang
Vision-language-action (VLA) and world-action models typically absorb unfamiliar manipulation tasks through additional robot data collection and policy optimization. This recurring…
Towards the Vision-Sound-Language-Action Paradigm: The HEAR Framework for Sound-Centric Manipulation
Chang Nie, Tianchen Deng, Guangming Wang +2
While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts or focus exclusively on human speech. T…
MID: A Self-supervised Multimodal Iterative Denoising Framework
Chang Nie, Tianchen Deng, Zhe Liu +1
Data denoising is a persistent challenge across scientific and engineering domains. Real-world data is frequently corrupted by complex, non-linear noise, rendering traditional rule…
MRASfM: Multi-Camera Reconstruction and Aggregation through Structure-from-Motion in Driving Scenes
Lingfeng Xuan, Chang Nie, Yiqing Xu +3
Structure from Motion (SfM) estimates camera poses and reconstructs point clouds, forming a foundation for various tasks. However, applying SfM to driving scenes captured by multi-…
MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep Thinking
Chang Nie, Yiqing Xu, Guangming Wang +3
Moving object segmentation plays a vital role in understanding dynamic visual environments. While existing methods rely on multi-frame image sequences to identify moving objects, s…