Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields
- URL: http://arxiv.org/abs/2311.12490v1
- Date: Tue, 21 Nov 2023 10:01:08 GMT
- Title: Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields
- Authors: Yifan Wang, Yi Gong and Yuan Zeng
- Abstract summary: We present Hyb-NeRF, a novel neural radiance field with a multi-resolution hybrid encoding.
We show that Hyb-NeRF achieves faster rendering speed with better rending quality and even a lower memory footprint in comparison to previous methods.
- Score: 12.335934855851486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in Neural radiance fields (NeRF) have enabled high-fidelity
scene reconstruction for novel view synthesis. However, NeRF requires hundreds
of network evaluations per pixel to approximate a volume rendering integral,
making it slow to train. Caching NeRFs into explicit data structures can
effectively enhance rendering speed but at the cost of higher memory usage. To
address these issues, we present Hyb-NeRF, a novel neural radiance field with a
multi-resolution hybrid encoding that achieves efficient neural modeling and
fast rendering, which also allows for high-quality novel view synthesis. The
key idea of Hyb-NeRF is to represent the scene using different encoding
strategies from coarse-to-fine resolution levels. Hyb-NeRF exploits
memory-efficiency learnable positional features at coarse resolutions and the
fast optimization speed and local details of hash-based feature grids at fine
resolutions. In addition, to further boost performance, we embed cone
tracing-based features in our learnable positional encoding that eliminates
encoding ambiguity and reduces aliasing artifacts. Extensive experiments on
both synthetic and real-world datasets show that Hyb-NeRF achieves faster
rendering speed with better rending quality and even a lower memory footprint
in comparison to previous state-of-the-art methods.
Related papers
- Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering [106.0057551634008]
We introduce an accurate and efficient few-shot neural rendering method named Spatial Annealing smoothing regularized NeRF (SANeRF)
By adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot NeRF methods.
arXiv Detail & Related papers (2024-06-12T02:48:52Z) - NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections [57.63028964831785]
Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content.
We address these issues with an approach based on ray tracing.
Instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts rays from these points and traces them through the NeRF representation to render feature vectors.
arXiv Detail & Related papers (2024-05-23T17:59:57Z) - VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations [25.88881764546414]
VQ-NeRF is an efficient pipeline for enhancing implicit neural representations via vector quantization.
We present an innovative multi-scale NeRF sampling scheme that concurrently optimize the NeRF model at both compressed and original scales.
We incorporate a semantic loss function to improve the geometric fidelity and semantic coherence of our 3D reconstructions.
arXiv Detail & Related papers (2023-10-23T01:41:38Z) - Efficient View Synthesis with Neural Radiance Distribution Field [61.22920276806721]
We propose a new representation called Neural Radiance Distribution Field (NeRDF) that targets efficient view synthesis in real-time.
We use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
Experiments show that our proposed method offers a better trade-off among speed, quality, and network size than existing methods.
arXiv Detail & Related papers (2023-08-22T02:23:28Z) - From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm [57.73868344064043]
We propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer.
We also present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer.
NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views.
arXiv Detail & Related papers (2023-06-10T09:19:19Z) - NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction [50.54946139497575]
We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering.
We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.
arXiv Detail & Related papers (2022-03-21T18:56:35Z) - NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling [82.99453001445478]
We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs.
Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron.
arXiv Detail & Related papers (2021-12-03T07:33:47Z) - Recursive-NeRF: An Efficient and Dynamically Growing NeRF [34.768382663711705]
Recursive-NeRF is an efficient rendering and training approach for the Neural Radiance Field (NeRF) method.
Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level.
Our evaluation on three public datasets shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality.
arXiv Detail & Related papers (2021-05-19T12:51:54Z) - FastNeRF: High-Fidelity Neural Rendering at 200FPS [17.722927021159393]
We propose FastNeRF, a system capable of rendering high fidelity images at 200Hz on a high-end consumer GPU.
The proposed method is 3000 times faster than the original NeRF algorithm and at least an order of magnitude faster than existing work on accelerating NeRF.
arXiv Detail & Related papers (2021-03-18T17:09:12Z)
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.