SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream
- URL: http://arxiv.org/abs/2403.11222v1
- Date: Sun, 17 Mar 2024 13:51:25 GMT
- Title: SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream
- Authors: Lin Zhu, Kangmin Jia, Yifan Zhao, Yunshan Qi, Lizhi Wang, Hua Huang,
- Abstract summary: Spike cameras offer distinct advantages over standard cameras.
Existing approaches reliant on spike cameras often assume optimal illumination.
We introduce SpikeNeRF, the first work that derives a NeRF-based volumetric scene representation from spike camera data.
- Score: 26.165424006344267
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spike cameras, leveraging spike-based integration sampling and high temporal resolution, offer distinct advantages over standard cameras. However, existing approaches reliant on spike cameras often assume optimal illumination, a condition frequently unmet in real-world scenarios. To address this, we introduce SpikeNeRF, the first work that derives a NeRF-based volumetric scene representation from spike camera data. Our approach leverages NeRF's multi-view consistency to establish robust self-supervision, effectively eliminating erroneous measurements and uncovering coherent structures within exceedingly noisy input amidst diverse real-world illumination scenarios. The framework comprises two core elements: a spike generation model incorporating an integrate-and-fire neuron layer and parameters accounting for non-idealities, such as threshold variation, and a spike rendering loss capable of generalizing across varying illumination conditions. We describe how to effectively optimize neural radiance fields to render photorealistic novel views from the novel continuous spike stream, demonstrating advantages over other vision sensors in certain scenes. Empirical evaluations conducted on both real and novel realistically simulated sequences affirm the efficacy of our methodology. The dataset and source code are released at https://github.com/BIT-Vision/SpikeNeRF.
Related papers
- Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling [70.34875558830241]
We present a way for learning a-temporal (4D) embedding, based on semantic semantic gears to allow for stratified modeling of dynamic regions of rendering the scene.
At the same time, almost for free, our tracking approach enables free-viewpoint of interest - a functionality not yet achieved by existing NeRF-based methods.
arXiv Detail & Related papers (2024-06-06T03:37:39Z) - NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild [55.154625718222995]
We introduce NeRF On-the-go, a simple yet effective approach that enables the robust synthesis of novel views in complex, in-the-wild scenes.
Our method demonstrates a significant improvement over state-of-the-art techniques.
arXiv Detail & Related papers (2024-05-29T02:53:40Z) - 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) - Spike-NeRF: Neural Radiance Field Based On Spike Camera [24.829344089740303]
We propose Spike-NeRF, the first Neural Radiance Field derived from spike data.
Instead of the multi-view images at the same time of NeRF, the inputs of Spike-NeRF are continuous spike streams captured by a moving spike camera in a very short time.
Our results demonstrate that Spike-NeRF produces more visually appealing results than the existing methods and the baseline we proposed in high-speed scenes.
arXiv Detail & Related papers (2024-03-25T04:05:23Z) - SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams [44.02794438687478]
Spike cameras have proven effective in capturing motion features and beneficial for solving this ill-posed problem.
Existing methods fall into the supervised learning paradigm, which suffers from notable performance degradation when applied to real-world scenarios.
We propose the first self-supervised framework for the task of spike-guided motion deblurring.
arXiv Detail & Related papers (2024-03-14T15:29:09Z) - Finding Visual Saliency in Continuous Spike Stream [23.591309376586835]
In this paper, we investigate the visual saliency in the continuous spike stream for the first time.
We propose a Recurrent Spiking Transformer framework, which is based on a full spiking neural network.
Our framework exhibits a substantial margin of improvement in highlighting and capturing visual saliency in the spike stream.
arXiv Detail & Related papers (2024-03-10T15:15:35Z) - Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion [67.15935067326662]
Event cameras offer low power, low latency, high temporal resolution and high dynamic range.
NeRF is seen as the leading candidate for efficient and effective scene representation.
We propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras.
arXiv Detail & Related papers (2023-09-15T17:52:08Z) - Recurrent Spike-based Image Restoration under General Illumination [21.630646894529065]
Spike camera is a new type of bio-inspired vision sensor that records light intensity in the form of a spike array with high temporal resolution (20,000 Hz)
Existing spike-based approaches typically assume that the scenes are with sufficient light intensity, which is usually unavailable in many real-world scenarios such as rainy days or dusk scenes.
We propose a Recurrent Spike-based Image Restoration (RSIR) network, which is the first work towards restoring clear images from spike arrays under general illumination.
arXiv Detail & Related papers (2023-08-06T04:24:28Z) - CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural
Representations [77.90883737693325]
This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views.
This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively.
In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
arXiv Detail & Related papers (2022-09-02T17:44:50Z) - InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation.
The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints.
We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
arXiv Detail & Related papers (2021-12-31T11:56:01Z)
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.