NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering
Abstract: Autonomous robotic tasks require actively perceiving the environment to achieve application-specific goals. In this paper, we address the problem of positioning an RGB camera to collect the most informative images to represent an unknown scene, given a limited measurement budget. We propose a novel mapless planning framework to iteratively plan the next best camera view based on collected image measurements. A key aspect of our approach is a new technique for uncertainty estimation in image-based neural rendering, which guides measurement acquisition at the most uncertain view among view candidates, thus maximising the information value during data collection. By incrementally adding new measurements into our image collection, our approach efficiently explores an unknown scene in a mapless manner. We show that our uncertainty estimation is generalisable and valuable for view planning in unknown scenes. Our planning experiments using synthetic and real-world data verify that our uncertainty-guided approach finds informative images leading to more accurate scene representations when compared against baselines.
Summary
- The paper presents a novel NBV planning framework that couples uncertainty estimation with neural rendering for active perception.
- It employs an LSTM-based adaptive ray sampling approach to predict view uncertainties and guide efficient image acquisition.
- Experimental evaluations on DTU and synthetic datasets demonstrate superior scene representation compared to heuristic methods.
NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering
Abstract
The paper "NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering" introduces a novel framework for active perception in robotics, focusing on sensor positioning to gather informative images for mapping unfamiliar environments. This work presents a mapless planning strategy that leverages uncertainty estimation in image-based neural rendering to effectively guide the acquisition of the next best view (NBV). The paper demonstrates that uncertainty estimation can generalize effectively to unknown scenes, improving scene representation accuracy against conventional baselines.
Introduction
Active perception plays an essential role in robotics, ensuring tasks like navigation and manipulation are successful by autonomously perceiving environmental details. The NeU-NBV framework addresses the challenge of optimally positioning an RGB camera to capture the most informative images for unknown scene representation, optimizing data collection within constrained measurement budgets.
Traditional NBV planning methods, reliant on explicit map representations such as point clouds or volumetric maps, face scalability limitations. NeU-NBV circumvents these by utilizing implicit neural representations, specifically neural radiance fields (NeRFs), for scene understanding through continuous function optimization of image data.
The proposed framework enhances previous models by incorporating uncertainty estimation directly in image-based neural rendering without requiring retraining – vital for practical robotic applications where computational resources are time-sensitive.
Framework Overview
The core contribution of NeU-NBV lies in coupling uncertainty estimation with neural rendering to guide NBV planning. The framework functions by continuously updating an image collection with measurements from selected views that pose high uncertainty. Rendering these uncertain views introduces maximum information into the exploration process.
Figure 1: Our novel NBV planning framework exploits uncertainty estimation in image-based neural rendering to guide measurement acquisition efficiently.
Network Architecture
NeU-NBV employs an architecture inspired by PixelNeRF but substitutes inefficient volume rendering with adaptive ray sampling for enhanced operational speed. The network features an LSTM module for real-time prediction of sampling points along camera rays, optimizing rendering resolution dynamically.
Figure 2: Our network architecture uses an LSTM module to efficiently predict jumping distances, enhancing feature aggregation from reference images.
Uncertainty Estimation
The uncertainty estimation technique models RGB values as logistic normal distributions, leveraging predicted variances to guide view selection. This probabilistic interpretation aligns well with real-world rendering errors, generalizing to unknown scenes without retraining requirements.
Figure 3: Examples illustrate our network effectively predicting rendering uncertainty based on reference image information quality.
Next Best View Planning Strategy
To implement NBV planning, a set of view candidates is evaluated based on the network-predicted per-pixel uncertainty. Selecting views with the highest uncertainty ensures maximum information value per measurement. This mapless framework allows efficient exploration without explicit scene mapping requirements, optimizing operational costs.
Figure 4: Overview of our mapless NBV planning framework, demonstrating how uncertainty guides measurement acquisition.
Experimental Evaluation
NeU-NBV framework's efficiency is validated through extensive experiments using real-world DTU and synthetic datasets, demonstrating clear superiority over heuristic baselines. Uncertainty-guided NBV planning consistently achieves better rendering performance in comparison to random or maximum distance-based view selections.
Figure 5: Comparison of NBV planners on the DTU dataset, indicating improved scene representation with uncertainty-guided images.
Implications and Future Work
NeU-NBV contributes significantly to active perception paradigms, providing a data-driven mapless approach to NBV planning. Its application is particularly relevant for real-time robotic tasks requiring adaptive scene exploration without intensive computational costs. Potential enhancements include incorporating depth inputs for faster sampling processes and semantic prediction integration for complex environment navigation.
Conclusion
The NeU-NBV framework successfully bridges active perception with robust neural rendering strategies. By integrating uncertainty estimation, it achieves superior scene representation accuracy with efficient data collection processes. As robotic perception evolves, methodologies such as NeU-NBV set noteworthy precedents in developing real-time active exploration systems. The paper provides substantial groundwork for expanding these strategies in future AI-driven applications.
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Glossary
- Active perception: A robotic strategy where the agent deliberately selects sensor viewpoints or actions to gather informative data for its goals. "Active perception and exploration is a core prerequisite for embodied robotic intelligence."
- Acquisition function: A scoring function used to evaluate and select among candidate actions or views based on expected utility or information gain. "using an acquisition function capturing their expected utility based on the current map state."
- Adam optimiser: An adaptive stochastic gradient optimization algorithm that uses estimates of first and second moments of gradients. "We use the Adam optimiser with a learning rate of and exponential decay of $0.999$."
- Area Under the Sparsification Error (AUSE): A metric that evaluates how well uncertainty correlates with error by progressively removing high-uncertainty pixels and measuring residual error. "we report the Area Under the Sparsification Error (AUSE) curve~\citep{Ilg2018}, which reveals how strongly the uncertainty coincides with the rendering error pixel-wise."
- Bilinear interpolation: A method for interpolating values on a 2D grid using linear interpolation in both directions, commonly used to sample image features. "by grid sampling with bilinear interpolation~\citep{Yu2021}."
- Differentiable volume rendering: A rendering technique that integrates radiance and density along rays with differentiable operations, enabling gradient-based learning. "The final RGB and depth estimate of the ray is calculated by differentiable volume rendering."
- Entropy (uncertainty measure): A measure of uncertainty in a probability distribution; here used to quantify ambiguity in density predictions along a ray. "propose calculating the entropy of the density prediction along the ray as an uncertainty measure with respect to the scene geometry."
- Ensemble (of models): A collection of models whose aggregated predictions can capture uncertainty via variance across the ensemble. "train an ensemble of NeRF models for a single scene, and measure uncertainty using the variance of the ensemble's prediction, which is utilised for NBV selection."
- Frustum: The pyramidal (or conical) volume representing a camera’s field of view in 3D space. "Given reference images from the current image collection of the scene (black frustums), our network outputs per-pixel uncertainty estimates at sampled view candidates (coloured frustums)."
- Image-based neural rendering: Methods that synthesize novel views by conditioning an implicit representation on features extracted from nearby images, without per-scene optimization. "another line of work focuses on image-based neural rendering~\citep{Yu2021, Rosu2021, Wang2021, Trevithick2021}."
- Implicit neural representations: Neural networks that parameterize continuous signals (e.g., radiance fields) implicitly in their weights, enabling differentiable query of values at arbitrary coordinates. "Implicit neural representations parameterise a continuous differentiable signal with a neural network~\citep{Tewari2022}."
- Instant-NGP: A fast framework for training and rendering neural graphics primitives (including NeRFs), often used to accelerate scene reconstruction. "we use Instant-NGP~\citep{mueller2022} to train NeRF models using images collected by the three planning approaches, respectively, under the same training conditions."
- Long short-term memory (LSTM): A recurrent neural network architecture that maintains long-range dependencies via gated memory cells. "we adopt a long short-term memory (LSTM) module~\citep{Hochreiter1997} to adaptively predict the jumping distance to the next sampling point"
- Logistic normal distribution: A distribution formed by applying the logistic (sigmoid) transform to a normal variable, yielding support on (0,1); used to model bounded channel values. "we model each channel value of the RGB prediction , where , as an independent logistic normal distribution described by:"
- Multilayer perceptron (MLP): A feedforward neural network composed of multiple layers of nonlinear transformations. "a multilayer perceptron (MLP) is trained to interpret the aggregated feature into appearance and geometry information at a novel view."
- Negative log-likelihood (NLL): A loss function that penalizes unlikely predictions under a probabilistic model, commonly used for maximum likelihood training. "we minimise the negative log-likelihood given ground truth RGB channel values"
- Neural radiance fields (NeRFs): Implicit models that learn a continuous 3D radiance and density field from images to render novel views via volume rendering. "NeRFs~\citep{Mildenhall2020} learn a density and radiance field supervised only by 2D images."
- Next Best View (NBV): The planning problem of selecting the next sensor viewpoint to maximize information gain given current knowledge. "we present a new framework for iteratively planning the next best view (NBV) for an RGB camera to explore an unknown scene."
- Peak signal-to-noise ratio (PSNR): A logarithmic metric (in dB) for image reconstruction quality based on mean squared error. "The rendering quality is measured by the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)~\citep{Mildenhall2020}."
- Positional encoding: A mapping that lifts coordinates into a higher-dimensional space (e.g., via sinusoids) to enable learning high-frequency variations. "the point position is mapped into higher-dimensional space by the positional encoding operation proposed by~\citet{Mildenhall2020}."
- Receding-horizon (planning): A strategy that optimizes actions over a finite horizon and re-plans at each step as new information becomes available. "find the NBV in a receding-horizon fashion by generating a random tree"
- Scene priors: Prior knowledge of scene structure learned across many scenes, enabling generalization to new environments. "This allows training the network across multiple scenes to learn scene priors, enabling it to generalise to new scenes without test time optimisation."
- Scene-centric hemisphere: An action space where viewpoints lie on a hemisphere centered around the scene, used for view sampling and planning. "For view planning, we consider a scene-centric hemisphere action space."
- Spearman's Rank Correlation Coefficient (SRCC): A non-parametric statistic measuring monotonic association between two ranked variables. "We use Spearman's Rank Correlation Coefficient (SRCC)~\citep{Spearman1904} to asses the monotonic relationship between averaged uncertainty estimate and rendering error over a test view."
- Structural similarity index measure (SSIM): An image quality metric assessing perceived structural similarity between images. "The rendering quality is measured by the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)~\citep{Mildenhall2020}."
- Uncertainty estimation: Quantifying the confidence of predictions, often to guide data acquisition or assess model reliability. "A key aspect of our approach is a new technique for uncertainty estimation in image-based neural rendering, which guides measurement acquisition at the most uncertain view among view candidates"
- Utility function: A scalar function that ranks candidate actions or views by expected benefit; used here to select NBV. "In this setup, we propose a simple utility function defined as:"
- Variational inference: An optimization-based approach to approximate complex posterior distributions in probabilistic models. "and uses variational inference to approximate their posterior distribution after training."
- Voxel map: A volumetric grid representation of 3D space where each voxel encodes occupancy or other properties. "\citet{Zaenker2021} maintain a voxel map of the scene and select the NBV among candidates obtained by targeted region-of-interest sampling and frontier-based exploration sampling."
- Volume rendering: Rendering method that integrates contributions along rays through a volumetric field (e.g., density, radiance). "PixelNeRF uses a volume rendering technique requiring dense sampling along the ray at predefined intervals"
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