- The paper introduces a novel method to estimate aleatoric uncertainty in surface normal estimation using an angular von Mises-Fisher distribution and a negative log-likelihood loss.
- A new decoder architecture employs uncertainty-guided sampling to refine pixel-wise predictions, particularly improving detail at object boundaries and intricate structures.
- The proposed approach significantly outperforms state-of-the-art models on ScanNet and NYUv2 datasets, achieving higher accuracy and a stronger correlation between estimated uncertainty and prediction error.
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
In the field of 3D scene understanding, surface normal estimation from single RGB images is a pivotal task, providing foundational insights for various applications like augmented reality and autonomous robotics. The paper "Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation" addresses significant limitations in current methods: the lack of integration of aleatoric uncertainty estimation and the paucity of detail in predictions. The research presents a novel network that predicts pixel-wise surface normal distributions and introduces methodological innovations to enhance the surface normal estimation task.
The core contribution of this work lies in its approach to estimating aleatoric uncertainty through the parameterization of per-pixel surface normal probability distributions. The authors propose an angular parameterization where the negative log-likelihood (NLL) loss represents the angular error with learned attenuation. This angular von Mises-Fisher (AngMF) distribution provides a robust framework for capturing inherent data noise, alongside measuring the expected angular error as an aleatoric uncertainty metric.
Additionally, the research introduces a novel decoder architecture enhanced by uncertainty-guided sampling. Pixel-wise refinement is achieved using multi-layer perceptrons (MLPs) trained selectively on pixels with high estimated uncertainty. This targeted sampling mechanism corrects the prevalent training bias towards extensive planar regions, thereby improving the prediction quality at object boundaries and within intricate structures.
The methodology outperformed state-of-the-art models like GeoNet++ and TiltedSN on rigorous benchmarks such as ScanNet and NYUv2 datasets. The proposed frameworks exhibited superior performance in key surface normal accuracy metrics, demonstrating significant advances in surface detail precision and uncertainty estimation reliability.
In validating the quality of uncertainty estimation, the paper adopts sparsification curves and evaluates metrics such as Area Under the Sparsification Curve (AUSC) and Area Under the Sparsification Error (AUSE). The results illustrate that the proposed AngMF distribution achieves a stronger correlation between estimated uncertainty and prediction error compared to conventional methods like dropout or test-time augmentation.
This research has profound implications for future developments in surface normal estimation and related 3D scene understanding tasks. By effectively incorporating aleatoric uncertainty estimation, the robustness, and reliability of models in handling real-world data imperfections are significantly enhanced. The introduction of uncertainty-guided refinement offers extensive potential for refining pixel-level predictions, which could be translated to other computer vision tasks involving complex spatial properties.
Looking ahead, opportunities for expanding this work could include integrating spatial rectifiers to address tilted images or incorporating multi-modal distribution frameworks to resolve the inherent ambiguity in monocular depth and normal estimation tasks. Overall, this paper sets a new standard in handling aleatoric uncertainty in surface normal estimation, significantly contributing to advancing methodologies in computer vision and algorithmic robustness.
