Robust Multi-Model Fitting through Learning Neighbor Regions
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. However, its performance is often limited by insufficient feature utilization, inefficient optimization, model overlap, and the non-differentiable pipelines. To overcome these limitations, we introduce a robust coarse-to-fine framework called Learning Neighbor Regions (LNR). Recognizing that substantial computational resources are wasted on numerous bad minimum sets, we propose the coarse-level module. This module utilizes a neural network to extract and analyze geometric feature of both local point-wise relations...
| Authors | Chang Nie, Guangming Wang, Zhe Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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