Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

• URL: http://arxiv.org/abs/2307.15333v1
• Date: Fri, 28 Jul 2023 06:21:42 GMT
• Title: Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF
• Authors: Haotian Bai, Yiqi Lin, Yize Chen, Lin Wang
• Abstract summary: We propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $&$ Temples, respectively.
• Score: 6.135925201075925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$ Temples, respectively. Project homepage:https://vlislab22.github.io/DOT. [1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

Related papers

• Efficient NeRF Optimization -- Not All Samples Remain Equally Hard [9.404889815088161]
  We propose an application of online hard sample mining for efficient training of Neural Radiance Fields (NeRF) NeRF models produce state-of-the-art quality for many 3D reconstruction and rendering tasks but require substantial computational resources.
  arXiv  Detail & Related papers  (2024-08-06T13:49:01Z)
• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields [54.8553158441296]
  We propose a novel visual localization framework, ie, PNeRFLoc, based on a unified point-based representation. On the one hand, PNeRFLoc supports the initial pose estimation by matching 2D and 3D feature points. On the other hand, it also enables pose refinement with novel view synthesis using rendering-based optimization.
  arXiv  Detail & Related papers  (2023-12-17T08:30:00Z)
• Adaptive Multi-NeRF: Exploit Efficient Parallelism in Adaptive Multiple Scale Neural Radiance Field Rendering [3.8200916793910973]
  Recent advances in Neural Radiance Fields (NeRF) have demonstrated significant potential for representing 3D scene appearances as implicit neural networks. However, the lengthy training and rendering process hinders the widespread adoption of this promising technique for real-time rendering applications. We present an effective adaptive multi-NeRF method designed to accelerate the neural rendering process for large scenes.
  arXiv  Detail & Related papers  (2023-10-03T08:34:49Z)
• Urban Radiance Field Representation with Deformable Neural Mesh Primitives [41.104140341641006]
  Deformable Neural Mesh Primitive(DNMP) is a flexible and compact neural variant of classic mesh representation. Our representation enables fast rendering (2.07ms/1k pixels) and low peak memory usage (110MB/1k pixels) We present a lightweight version that can run 33$times$ faster than vanilla NeRFs, and comparable to the highly-optimized Instant-NGP (0.61 vs 0.71ms/1k pixels)
  arXiv  Detail & Related papers  (2023-07-20T11:24:55Z)
• Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time [43.0484840009621]
  Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings. We present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural modeling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting. We show that the proposed method is 3000 times faster than the original NeRF and over an order of magnitude acceleration over SOTA.
  arXiv  Detail & Related papers  (2022-02-17T11:57:01Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• PnP-DETR: Towards Efficient Visual Analysis with Transformers [146.55679348493587]
  Recently, DETR pioneered the solution vision tasks with transformers, it directly translates the image feature map into the object result. Recent transformer-based image recognition model andTT show consistent efficiency gain.
  arXiv  Detail & Related papers  (2021-09-15T01:10:30Z)
• Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
  We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids. The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
  arXiv  Detail & Related papers  (2021-07-18T09:05:16Z)
• NeRF in detail: Learning to sample for view synthesis [104.75126790300735]
  Neural radiance fields (NeRF) methods have demonstrated impressive novel view synthesis. In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a performance and not trained end-to-end for the task at hand. We introduce a differentiable module that learns to propose samples and their importance for the fine network, and consider and compare multiple alternatives for its neural architecture.
  arXiv  Detail & Related papers  (2021-06-09T17:59:10Z)

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.