Improving multi model fitting accuracy and efficiency using neural networks
Robust Multi-Model Fitting through Learning Neighbor Regions
Computer Vision and Pattern Recognition
Summary
Multi-model fitting is about finding multiple shapes or patterns inside noisy data, but it often struggles because it wastes effort on bad guesses and can’t easily handle overlapping models. The authors propose a two-step method called Learning Neighbor Regions that first uses a neural network to pick good candidate model guesses and then refines them by looking at nearby data points. This makes the process both faster and more accurate. Their experiments show it works well across several common tasks that need to find multiple models from noisy points.
What this means in practice
- •For computer vision engineers: Improve scene reconstruction algorithms by more accurately identifying multiple geometric models within noisy data.
- •For augmented reality developers: Enhance mixed reality systems by robustly fitting multiple models to sensor inputs despite overlapping features and noise.
Authors
Chang Nie, Guangming Wang, Zhe Liu, Hesheng Wang
Abstract
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 relationships and global contextual information in minimum sets, outputting confidence to pre-select a small number of good minimum sets, thereby enhancing overall efficiency before solving hypotheses. To address model overlap, LNR encodes neighbor region features for each hypothesis in its fine-level module. These region features consist of geometric features of neighboring data points, which can be used by multiple regions simultaneously. This design allows the neural network to individually refine and score each hypothesis. Importantly, LNR is trained to learn directly from data point features rather than from the hypothesis parameters, thus avoiding differentiating the sampling process and the model solvers. Extensive experiments on four classic multi-model fitting tasks demonstrate that LNR achieves state-of-the-art performance. The analysis suggests that LNR can be easily adapted to various robust multi-model fitting tasks.