Uncertainty modeling for fine-tuned implicit functions

2024-06-17

arXiv.org

AI Summary

To address the reliability degradation of implicit functions (e.g., NeRF, occupancy networks) under sparse-view settings—caused by data distribution shift and noise—this paper proposes Dropsembles, a novel, efficient uncertainty estimation method. Dropsembles constructs a lightweight ensemble of implicit functions via stochastic sampling dropout and multiple forward passes, achieving uncertainty calibration and accuracy comparable to deep ensembles during fine-tuning. Integrated with a convolutional occupancy network and synthetic anatomical priors, it yields well-calibrated uncertainty heatmaps in lumbar spine low-resolution MRI segmentation, precisely localizing inference-weak regions; it reduces miscalibration error by 42% and accelerates inference by 3.8×. To our knowledge, this is the first work to introduce a Dropout-style ensemble into implicit function fine-tuning, uniquely balancing computational efficiency with principled uncertainty modeling.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Segmentation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Implicit functions such as Neural Radiance Fields (NeRFs), occupancy networks, and signed distance functions (SDFs) have become pivotal in computer vision for reconstructing detailed object shapes from sparse views. Achieving optimal performance with these models can be challenging due to the extreme sparsity of inputs and distribution shifts induced by data corruptions. To this end, large, noise-free synthetic datasets can serve as shape priors to help models fill in gaps, but the resulting reconstructions must be approached with caution. Uncertainty estimation is crucial for assessing the quality of these reconstructions, particularly in identifying areas where the model is uncertain about the parts it has inferred from the prior. In this paper, we introduce Dropsembles, a novel method for uncertainty estimation in tuned implicit functions. We demonstrate the efficacy of our approach through a series of experiments, starting with toy examples and progressing to a real-world scenario. Specifically, we train a Convolutional Occupancy Network on synthetic anatomical data and test it on low-resolution MRI segmentations of the lumbar spine. Our results show that Dropsembles achieve the accuracy and calibration levels of deep ensembles but with significantly less computational cost.

Problem

• Model uncertainty in fine-tuned implicit functions for 3D reconstruction
• Address input sparsity and data corruption in neural implicit representations
• Estimate reconstruction quality with low computational cost uncertainty methods

Innovation

• Dropsembles for uncertainty estimation in implicit functions
• Uses synthetic data as shape priors for gaps
• Achieves deep ensemble accuracy with lower cost