# A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

arXiv: https://arxiv.org/abs/2311.01842 · HTML: https://mlsanity.com/paper/2311.01842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields](https://mlsanity.com/paper/2210.15947/) (2022)
- [LOLNeRF: Learn from One Look](https://mlsanity.com/paper/2111.09996/) (2021)
- [Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2209.08409/) (2022)
- [X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360circ  Insufficient RGB-D Views](https://mlsanity.com/paper/2210.05135/) (2022)
- [Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing](https://mlsanity.com/paper/2304.10836/) (2023)
- [Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection](https://mlsanity.com/paper/2304.10833/) (2023)
- [IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet](https://mlsanity.com/paper/2304.10840/) (2023)
- [Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm](https://mlsanity.com/paper/2304.14605/) (2023)
- [Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection](https://mlsanity.com/paper/2309.00824/) (2023)
- [Optimal Base Station Antenna Downtilt in Downlink Cellular Networks](https://mlsanity.com/paper/1802.07479/) (2018)
- [Routing Approach for P2P Systems Over MANET Network](https://mlsanity.com/paper/2101.03109/) (2020)

## Abstract (arXiv)

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.