3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe splats in splats, the first 3DGS steganography framework that embeds 3D content in 3DGS itself without modifying any attributes. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that our method significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3x faster rendering speed, while ensuring security, robustness, and user experience.

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization.

High-speed vision tasks have long been a challenge in computer vision. Recently, the spike camera has shown great potential in these tasks due to its high temporal resolution. Unlike traditional cameras, it emits asynchronous spike signals to capture visual information. However, under low-light conditions, spike signals become highly sparse, and the sparse spike stream severely hinders the effectiveness of existing spike-based methods in high-speed scenarios. To address this challenge, we introduce SS2DS, the first deep learning framework that enhances sparse spike streams into dense spike streams. SS2DS first estimates the spike firing frequency within sparse streams. Subsequently, the spike firing frequency is enhanced by a neural network. Finally, SS2DS decodes the enhanced spike stream from the enhanced spike firing frequency sequence. SS2DS can adjust the temporal distribution of sparse spike streams and improve the performance degradation of existing methods in low-light and high-speed scenarios. To evaluate sparse spike stream enhancement, we construct both synthetic and real sparse spike stream datasets. By comparing the reconstruction results, enhanced spike streams achieve an average improvement of +0.78 MA, -18.42 BRISQUE, and -1.42 NIQE over sparse spike streams. Moreover, the enhanced spike streams also benefit other spike-based vision tasks, such as 3D reconstruction (+1.325 dB PSNR, +0.005 SSIM, and -0.01 LPIPS) and superresolution (+0.63 MA, -13.67 BRISQUE, and -1.28 NIQE).

3D Gaussian Splatting (3DGS) has been proven to exhibit exceptional performance in reconstructing 3D scenes. However, the effectiveness of 3DGS heavily relies on sharp images, and fulfilling this requirement presents challenges in real-world scenarios particularly when utilizing fast-moving cameras. This limitation severely constrains the practical application of 3DGS and may compromise the feasibility of real-time reconstruction. To mitigate these challenges, we proposed Spike Gaussian Splatting (SpikeGS), the first framework that integrates the Bayer-pattern spike streams into the 3DGS pipeline to reconstruct 3D scenes captured by a fast-moving high temporal color spike camera in one second. With accumulation rasterization, interval supervision, and a special designed pipeline, SpikeGS realizes continuous spatiotemporal perception while extracts detailed structure and texture from Bayer-pattern spike stream which is unstable and lacks details. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of SpikeGS compared with existing spike-based and deblur 3D scene reconstruction methods.

As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking. Inspired by the success of the spike camera, we proposed Spike-NeRF, the first Neural Radiance Field derived from spike data, to achieve 3D reconstruction and novel viewpoint synthesis for high-speed scenes. Instead of the multi-view images at the same as time of NeRF, the inputs of Spike-NeRF are continuous spike streams captured by a moving spike camera in a very short time. To reconstruct a correct and stable 3D scene from high-frequency but unstable spike data, we devised spike masks along with a distinctive loss function. We evaluate our method qualitatively and quantitatively on several challenging synthetic scenes generated using Blender with the spike camera simulator. Our results demonstrate that Spike-NeRF produces more visually appealing results than the existing methods and the baseline we proposed in high-speed scenes. Our code is available at https://github.com/yijiaguo02/SpikeNerf

Spike cameras, with their exceptional temporal resolution, are revolutionizing high-speed visual applications. Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction and optical flow. However, current synthetic datasets for spike cameras lack sophistication. Addressing this gap, we introduce SCSim, a novel and more realistic spike camera simulator with a comprehensive noise model. SCSim is adept at autonomously generating driving scenarios and synthesizing corresponding spike streams. To enhance the fidelity of these streams, we’ve developed a comprehensive noise model tailored to the unique circuitry of spike cameras. Our evaluations demonstrate that SCSim outperforms existing simulation methods in generating authentic spike streams. Crucially, SCSim simplifies the creation of datasets, thereby greatly advancing spike-based visual tasks like reconstruction. Our project refers to https://github.com/Acnext/SCSim.

We proposed Precomputed Radiance Transfer of Gaussian Splats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer. Existing studies have demonstrated that 3D Gaussian splatting (3DGS) outperforms neural fields in efficiency for dynamic lighting scenarios. However, the current relighting method based on 3DGS is still struggling to compute high-quality shadow and indirect illumination in real time for dynamic light, leading to unrealistic rendering results. We solve this problem by precomputing the expensive transport simulations required for complex transfer functions like shadowing, the resulting transfer functions are represented as dense sets of vectors or matrices for every Gaussian splat. We introduce distinct precomputing methods tailored for training and rendering stages, along with unique ray tracing and indirect lighting precomputation techniques for 3D Gaussian splats to accelerate training speed and compute accurate indirect lighting related to environment light. Experimental analyses demonstrate that our approach achieves state-of-the-art visual quality while maintaining competitive training times and importantly allows high-quality real-time (30+ fps) relighting for dynamic light and relatively complex scenes at 1080p resolution.

Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging. The increasing frame rate is usually guaranteed by using shorter exposure time so that the captured video is severely interfered by noise. Alternating exposures can alleviate the noise issue but sacrifice frame rate due to involving long-exposure frames. The neuromorphic spiking camera records high-speed scenes of high dynamic range without colors using a completely different sensing mechanism and visual representation. We introduce a hybrid camera system composed of a spiking and an alternating-exposure RGB camera to capture HFR&HDR scenes with high fidelity. Our insight is to bring each camera's superiority into full play. The spike frames, with accurate fast motion information encoded, are firstly reconstructed for motion representation, from which the spike-based optical flows guide the recovery of missing temporal information for long-exposure RGB images while retaining their reliable color appearances. With the strong temporal constraint estimated from spike trains, both missing and distorted colors cross RGB frames are recovered to generate time-consistent and HFR color frames. We collect a new Spike-RGB dataset that contains 300 sequences of synthetic data and 20 groups of real-world data to demonstrate 1000 FPS HDR videos outperforming HDR video reconstruction methods and commercial high-speed cameras.

As a bio-inspired sensor with high temporal resolution, the spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. However, frame-based and event-based methods are not well suited to spike streams from the spiking camera due to the different data modalities. To this end, we present, SCFlow, a tailored deep learning pipeline to estimate optical flow in high-speed scenes from spike streams. Importantly, a novel input representation is introduced which can adaptively remove the motion blur in spike streams according to the prior motion. Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic Highspeed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes. Experimental results show that the SCFlow can predict optical flow from spike streams in different high-speed scenes. Moreover, SCFlow shows promising generalization on real spike streams. Codes and datasets refer to https://github.com/Acnext/Optical-Flow-For-Spiking-Camera.