NARUTO: Neural Active Reconstruction from Uncertain Target Observations

• URL: http://arxiv.org/abs/2402.18771v2
• Date: Tue, 16 Apr 2024 22:15:58 GMT
• Title: NARUTO: Neural Active Reconstruction from Uncertain Target Observations
• Authors: Ziyue Feng, Huangying Zhan, Zheng Chen, Qingan Yan, Xiangyu Xu, Changjiang Cai, Bing Li, Qilun Zhu, Yi Xu,
• Abstract summary: We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning. Our system autonomously explores by targeting uncertain observations and reconstructs environments with remarkable completeness and fidelity.
• Score: 30.09067122521648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction. Our approach leverages a multi-resolution hash-grid as the mapping backbone, chosen for its exceptional convergence speed and capacity to capture high-frequency local features.The centerpiece of our work is the incorporation of an uncertainty learning module that dynamically quantifies reconstruction uncertainty while actively reconstructing the environment. By harnessing learned uncertainty, we propose a novel uncertainty aggregation strategy for goal searching and efficient path planning. Our system autonomously explores by targeting uncertain observations and reconstructs environments with remarkable completeness and fidelity. We also demonstrate the utility of this uncertainty-aware approach by enhancing SOTA neural SLAM systems through an active ray sampling strategy. Extensive evaluations of NARUTO in various environments, using an indoor scene simulator, confirm its superior performance and state-of-the-art status in active reconstruction, as evidenced by its impressive results on benchmark datasets like Replica and MP3D.

Related papers

• HERE: Hierarchical Active Exploration of Radiance Field with Epistemic Uncertainty Minimization [21.297877967566766]
  We present HERE, an active 3D scene reconstruction framework based on neural radiance fields, enabling high-fidelity implicit mapping.<n>Our approach centers around an active learning strategy for camera trajectory generation, driven by accurate identification of unseen regions.<n>The effectiveness of the proposed method in active 3D reconstruction is demonstrated by achieving higher reconstruction completeness compared to previous approaches.
  arXiv  Detail & Related papers  (2026-01-12T06:23:29Z)
• Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representations [0.0]
  We investigate the use of of-Implicit Neural Representations for dynamic X-ray computed tomography (XCT) reconstruction under interlaced acquisition schemes.<n>The proposed approach combines ADMM-based optimization with INCODE, a conditioning framework incorporating prior knowledge, to enable efficient convergence.<n>Across all settings, our model achieves strong performance robustness and outperforms Time-Inter Model-Based Iter Reconstruction (TIMBIR), a state-of-the-art model-based iterative method.
  arXiv  Detail & Related papers  (2025-10-09T01:33:58Z)
• Certified Neural Approximations of Nonlinear Dynamics [52.79163248326912]
  In safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system.<n>We propose a novel, adaptive, and parallelizable verification method based on certified first-order models.
  arXiv  Detail & Related papers  (2025-05-21T13:22:20Z)
• Risk-Averse Certification of Bayesian Neural Networks [70.44969603471903]
  We propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN. We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method.
  arXiv  Detail & Related papers  (2024-11-29T14:22:51Z)
• Neural Experts: Mixture of Experts for Implicit Neural Representations [41.395193251292895]
  Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. We propose a mixture of experts (MoE) implicit neural representation approach that enables learning local piece-wise continuous functions. We show that incorporating a mixture of experts architecture into existing INR formulations provides a boost in speed, accuracy, and memory requirements.
  arXiv  Detail & Related papers  (2024-10-29T01:11:25Z)
• Active Neural Mapping at Scale [27.236684479724495]
  We introduce a NeRF-based active mapping system that enables efficient and robust exploration of large-scale indoor environments. Key to our approach is the extraction of a generalized Voronoi graph (GVG) from the continually updated neural map.
  arXiv  Detail & Related papers  (2024-09-30T13:27:41Z)
• Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM [17.661231232206028]
  Simultaneous localization and mapping (SLAM) with implicit neural representations has received extensive attention. We propose a novel SLAM framework for dynamic environments.
  arXiv  Detail & Related papers  (2024-07-18T09:35:48Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• A Novel Feature Learning-based Bio-inspired Neural Network for Real-time Collision-free Rescue of Multi-Robot Systems [5.478000072204037]
  A bio-inspired neural network is proposed to generate a rescue path in complex and dynamic environments. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations.
  arXiv  Detail & Related papers  (2024-03-13T04:43:10Z)
• Robust Neural Pruning with Gradient Sampling Optimization for Residual Neural Networks [0.0]
  This research embarks on pioneering the integration of gradient sampling optimization techniques, particularly StochGradAdam, into the pruning process of neural networks. Our main objective is to address the significant challenge of maintaining accuracy in pruned neural models, critical in resource-constrained scenarios.
  arXiv  Detail & Related papers  (2023-12-26T12:19:22Z)
• Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images [79.07468367923619]
  We develop convolutional neural generative coding (Conv-NGC) We implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps. We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising.
  arXiv  Detail & Related papers  (2022-11-22T06:42:41Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Attribute-Guided Adversarial Training for Robustness to Natural Perturbations [64.35805267250682]
  We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
  arXiv  Detail & Related papers  (2020-12-03T10:17:30Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.