Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differentiable quantization to discretize the Gaussian attributes of Scaffold-GS. Subsequently, a Laplacian-based rate proxy is devised to impose an entropy constraint, guiding the generation of high-fidelity and compact reconstructions. Finally, this integer-type Gaussian point cloud is compressed losslessly using Octree and run-length coding. Experiments validate that the proposed rate proxy accurately estimates the bitrate of run-length coding, enabling Voxel-GS to eliminate redundancy and optimize for a more compact representation. Consequently, our method achieves a remarkable compression ratio with significantly faster coding speeds than prior art. The code is available at https://github.com/zb12138/VoxelGS.

3D Gaussian splatting enables real-time, photo-realistic scene rendering but is challenged by redundant, high-dimensional primitive data. To make immersive AR/VR and volumetric communication practical, compression must address both temporal and attribute redundancy. We introduce a streamlined inter-prediction coding pipeline that predicts each P-frame's color and geometry attributes from a nearby reference using a lightweight hybrid predictor, combining bilateral filtering and K-nearest-neighbor feature transfer, and encodes only residuals. Our approach targets the most rate-critical channels by applying codebook-based vector quantization to spherical harmonic components, while retaining efficient scalar quantization for other attributes. All quantized streams and codebooks are entropy coded. The proposed method achieves an average 89.1 % reduction in BD-rate and a 12.39 dB increase in BD-PSNR over GPCCv1, and outperforms previous SOTA interGS by 18.6 % in BD-rate and 1.81 dB in BD-PSNR. With practical decoding speeds (0.605 seconds per frame), interGS-Lite preserves real-time rendering and has potential for streaming and AR/VR applications, delivering consistent bitrate reductions with high visual quality.

A point cloud is a 3D data representation that presents unique challenges due to its large volume, high dimensionality, and lack of structure. This work introduces FDIP-PCAC, a$F$requency$D$omain$I$nter$P$rediction and Compensation system for dynamic point cloud attribute compression. The method combines deep learning, geometry-driven modeling, and Graph Fourier Transform (GFT) within a five-phase encoder–decoder framework. Initially, the reference and target point cloud frames are partitioned into corresponding nodes using a binary tree. In Phase 1, each target node identifies its K-nearest reference nodes based on the geometry centroid. Phase 2 computes the GFT latent representation of these selected reference nodes. Phase 3 and 4 transfer the RGB colors from matched reference nodes to target nodes, generating Predicted Target Node and Second-Stage Predicted Target Node, respectively. In phase 5, the neural network-based FreqNeRF predicts the GFT latent representation of target nodes. The proposed system achieves superior compression and reconstruction performance compared to G-PCC across diverse datasets. It delivers average BD-rate reductions of 65.89% against RAHT-v30, 63.61% against PredLift-v30, 67.59% against RAHT-v23 and 66.82% against PredLift-v23.

Current video-based GS compression methods rely on using Parallel Linear Assignment Sorting (PLAS) to convert 3D GS into smooth 2D maps, which are computationally expensive and time-consuming, limiting the application of GS on lightweight devices. In this paper, we propose a Lightweight 3D Gaussian Splatting (GS) Compression method based on Video codec (LGSCV). First, a two-stage Morton scan is proposed to generate blockwise 2D maps that are friendly for canonical video codecs in which the coding units (CU) are square blocks. A 3D Morton scan is used to permute GS primitives, followed by a 2D Morton scan to map the ordered GS primitives to 2D maps in a blockwise style. However, although the blockwise 2D maps report close performance to the PLAS map in high-bitrate regions, they show a quality collapse at medium-to-low bitrates. Therefore, a principal component analysis (PCA) is used to reduce the dimensionality of spherical harmonics (SH), and a MiniPLAS, which is flexible and fast, is designed to permute the primitives within certain block sizes. Incorporating SH PCA and MiniPLAS leads to a significant gain in rate-distortion (RD) performance, especially at medium and low bitrates. MiniPLAS can also guide the setting of the codec CU size configuration and significantly reduce encoding time. Experimental results on the MPEG dataset demonstrate that the proposed LGSCV achieves over 20% RD gain compared with state-of-the-art methods, while reducing 2D map generation time to approximately 1 second and cutting encoding time by 50%. The code is available at https://github.com/Qi-Yangsjtu/LGSCV.

High-fidelity 3D representations of objects and scenes can be obtained with point clouds, but dealing with their massive data sizes can be difficult. This data is efficiently compressed via MPEG’s Geometry-based Point Cloud Compression (G-PCC), which makes it manageable and useful for real-world applications. One major drawback, though, is that decoding introduces coding artifacts that cause the reconstructed point cloud to appear blocky. In this paper, we present a new approach to attribute learning in point clouds leveraging sparse convolution, that effectively deals with the non-uniformity and sparsity of these data structures.

Remote sensing scene classification aims to assign correct semantic labels to remote sensing images. Many state-of-the-art algorithms have made significant contributions to improving model accuracy. However, these algorithms often involve a large number of parameters and floating-point computations. To address this, we propose a lightweight network architecture named RandommaskFormer. This network initially utilizes random position filtering to reduce model complexity. Next, effective feature interactions are achieved through feature covariance analysis. Finally, we implement a two-stage optimization strategy, incorporating both label optimization and loss function optimization. Among the above strategy, we first perform clustering analysis on remote sensing image features using a KD tree to assign new visual labels. Then, we combine triplet loss with cross-entropy loss to guide model training. Experimental results on three mainstream datasets demonstrate the effectiveness of RandommaskFormer. Additionally, the deployment of the two-stage optimization strategy further improves the model's performance.

Noise interference often occurs, during the collection of point cloud data, which can significantly affect the original color characteristics of the data. Currently, point cloud color denoising methods are usually based on graph structures or filters, whose denoising effects are generally influenced by the construction of the graph or the choice of filter. Different from them, this paper proposes a deep learning network for point cloud color denoising that combines an implicit neural autoencoder with a multi-frame sparse convolutional learning. We propose a novel sparse feature extraction module based on sparse convolution. The use of sparse convolution allows only processing the features at the effective positions of the point cloud, significantly improving computational efficiency. Moreover, considering that valuable information can gradually be diluted through sparse convolution, a pretrained implicit neural autoencoder is employed to map the input point cloud features to a more informative high-dimensional space, allowing for more thorough network learning. Furthermore, we propose a new method for processing point cloud data that compacts the point cloud while retaining all its color features. Finally, to further utilize the spatiotemporal information of the point cloud, a multi-frame fusion algorithm is also proposed. Experimental results show that compared with state-of-the-art algorithms, our proposed method achieves better denoising performance where the average PSNR of denoised point cloud is improved up to 1.14 dB.

Subspace Indexing with Interpolation (SIM-I) on Stiefel and Grassmann manifolds is proposed in this work. Given a partition of some original high-dimensional data set, SIM-I is constructed via two steps: in the first step we build linear affinity-aware subspace models based on each partition; in the second step we interpolate between several adjacent linear subspace models constructed in the first step using the “center of mass” calculation on Stiefel and Grassmann manifolds. Through these two steps, SIM-I builds a globally nonlinear and smoothly regularized low-dimensional embedding model of the original data set. Furthermore, given sufficiently many training samples on the data manifold either labelled by some pre-trained learning model such as Deep Neural Networks (DNNs) or provided with original natural labels, we first apply SIM-I on this data set and then perform nearest-neighbor classification on the resulting low-dimensional embedding. This helps us to build a Lightweight Inference Engine (LIE) carrying similar level of feature extraction by the pre-trained learning model. For DNNs, such LIE can be interpreted as some (nonstandard) shallow neural network with a wide first hidden layer. From this perspective, SIM-I provides a way to exchange deep network for wide but shallow ones and may provide some new insights to interpret DNNs.

Point clouds offer the realistic three-dimensional (3-D) representation of objects or scenes at the expense of high data volume. To compactly represent such data in real-world applications, Video-based Point Cloud Compression (V-PCC) converts them into two-dimensional (2-D) attribute maps before lossy compression. However, the coding artifacts introduced in the decoded attribute maps eventually bring texture degradation in the reconstructed point cloud. In this paper, we propose a deep-learning based attribute map enhancement method by fully leveraging the guidance of the occupancy map in local feature modification and non-local attention for capturing long-range spatial correlations.

Recent years have witnessed the growth of point cloud based applications for both immersive media as well as 3D sensing for auto-driving, because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challenging problem to compress sparse, unstructured, and high-precision 3D points for efficient communication. In this paper, leveraging the sparsity nature of the point cloud, we propose a multiscale end-to-end learning framework that hierarchically reconstructs the 3D Point Cloud Geometry (PCG) via progressive re-sampling. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction. For the input PCG which has only the binary occupancy attribute, our framework translates it to a down-scaled point cloud at the bottleneck layer which possesses both geometry and associated feature attributes. Then, the geometric occupancy is losslessly compressed using an octree codec and the feature attributes are lossy compressed using a learned probabilistic context model. Compared with the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based PCC (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG), our method achieves more than 40% and 70% BD-Rate (BjØntegaard Delta Rate) reduction, respectively. We would like to make all materials publicly accessible at https://njuvision.github.io/PCGCv2/ for reproducible research.

In this paper, we first extend the video-based point cloud compression (V-PCC) to support the plenoptic point cloud compression by generating multiple attribute videos. Then based on the observation that these videos from multiple views have very high correlations, we propose encoding them using multiview high efficiency video coding. We further propose a block-based padding method that unifies the unoccupied attribute pixels from different views to reduce their bit cost.

Point cloud media representation format has provided various opportunities for extended reality applications and had become widely used in volumetric content capturing scenarios. At the same time ambiguous storage format representations and network throughput are key problems for wide adoption of this media format. Compression algorithms in corresponding standard activities are aimed to solve this problem. MPEG-I standard has an aim of creating the point cloud compression methodology relying on existing video coding hardware implementations. In scope of the state-of-the-art video-based dynamic point cloud (DPC) compression method, similar 3D patches may be projected in totally different 2D positions in different frames. In this way, the motion vector predictors especially those in the patch boundary may be very inaccurate which may lead to significant bitrate increase. In this paper, we propose to use the reconstructed geometry information to help predict the motion vector more accurately and improve the coding efficiency of the attribute video. First, we propose to use the motion vector of the co-located blocks in the geometry frame as a merge candidate of the current block in the attribute frame. Second, we perform a motion estimation between the current reconstructed point cloud with only the geometry information and the reference point cloud to find the corresponding block. The motion information derived is used as motion vector predictor of the current block in the attribute frame. As far as we can see, this is the first work using the geometry information to compress the attribute in the DPC compression scenario. Significant compression efficiency is achieved with this new 3D point cloud geometry derived motion prediction scheme when compared with the state-of-the-art DPC compression method.

Point clouds are one of the emerging 3D visual representations of real word and plenty of useful applications has already been demonstrated. However, a huge amount of data associated with it has added challenges in both transmission and storage. This requires an efficient coding solution and brought a great attention among compression community. MPEG and JPEG standardization group has already started developing coding solution and proposed two test-models namely V-PCC, video-based coding solution, for dynamic point cloud and G-PCC, a native geometry-based coding solution, for static and LiDAR point cloud. In G-PCC, octree (lossless) and tri-soup(lossy) for geometry coding, similarly regional adaptive hierarchical transform (RAHT) and lifting-scheme for attributes coding are currently being explored. Lifting-scheme relies on level-of-details(LOD) structure for attributes prediction where LOD is generated with distance based subsampling approach. In this work we proposed a new LOD generation scheme using binary-tree and showed it provides better coding solution for sparse point cloud such as LiDAR. The experimental results demonstrated 12% bitrate reduction for reflectance and 8%, 6% and 7% bitrate reduction for luma, chroma Cb and chroma Cr respectively as well as up to 4 times computational complexity reduction compared to current G-PCC lifting-scheme.

In this paper, based on the Hessian approximation, an incremental pruning method is proposed to compress the deep neural network. The proposed method starts from the idea of using the Hessian to measure the "importance" of each weight in a deep neural network, and it mainly has the following key contributions. First, we propose to use the second moment in Adam optimizer as a measure of the "importance" of each weight to avoid calculating the Hessian matrix. Second, an incremental method is proposed to prune the neural network step by step. The incremental method can adjust the remaining non-zero weights of the whole network after each pruning to help boost the performance of the pruned network. Last but not least, the proposed method applies an automatically-generated global threshold for all the weights among all the layers, which achieves the inter-layer bit allocation automatically. Such a method can improve performance and save the complexity of adjusting the pruning threshold layer by layer. We perform a number of experiments on MNIST and ImageNet using commonly used neural networks such as AlexNet and VGG16 to show the benefits of the proposed algorithm. The experimental results show that the proposed algorithm is able to compress the network significantly with almost no loss of accuracy, which demonstrates the effectiveness of the proposed algorithm.

The Joint Video Exploration Team (JVET) recently launched the standardization of next-generation video coding named Versatile Video Coding (VVC) in which the Adaptive Multiple Transforms (AMT) is adopted as the primary residual coding transform solution. AMT introduces multiple transforms selected from the DST/DCT families and achieves noticeable coding gains. However, the set of transforms are calculated using direct matrix multiplication which induces higher run-time complexity and limits the application for practical video codec. In this paper, a fast DST-VII/DCT-VIII algorithm based on partial butterfly with dual implementation support is proposed, which aims at achieving reduced operation counts and run-time cost meanwhile yield almost the same coding performance. The proposed method has been implemented on top of the VTM-1.1 and experiments have been conducted using Common Test Conditions (CTC) to validate the efficacy. The experimental results show that the proposed methods, in the state-of-the-art codec, can provide an average of 7%, 5% and 8% overall decoding time savings under All Intra (AI), Random Access (RA) and Low Delay B (LDB) configuration, respectively yet still maintains coding performance.

Block-based transform coding in its nature causes blocking artifacts, which severely degrades picture quality especially in a high compression rate. Although convolutional neural networks (CNNs) achieve good performance in image restoration tasks, existing methods mainly focus on deep or efficient network architecture. The gradient of compressed images has different characteristics from the original gradient that has dramatic changes in pixel values along block boundaries. Motivated by them, we propose gradient guided image deblocking based on CNNs in this paper. Guided by the gradient information of the input blocky image, the proposed network successfully preserves textural edges while reducing blocky edges, and thus restores the original clean image from compression degradation. Experimental results demonstrate that the gradient information in the input compressed image contributes to blocking artifact reduction as well as the proposed method achieves a significant performance improvement in terms of visual quality and objective measurements.

Point cloud has shown great potential in many different applications including visual communication and auto-navigation systems. However, the immense size of the point cloud has been a bottleneck for its popularity in the consumer level. In this paper, we address the lossless compression of positional data of dynamic point cloud by viewing the point prediction of point cloud as Travelling Salesman Problem (TSP).

In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-field image compression method.

Key point features are very effective tools in image matching and key point feature aggregation is an effective scheme for creating a compact representation of the images for visual search. This solution not only achieves compression, but also offers the benefits of better accuracy in matching and indexing efficiency. Research is active in this area and recent results on Fisher Vector based aggregation have shown to be very effective in a number of application scenarios. In this paper, we present a new direct aggregation scheme that is adaptive to the descriptor distributions from individual images and does not enforce a single generative model such as GMM in the Fisher Vector type aggregation. Moreover, it achieves better compression as well as image matching accuracy. Simulation results with the image identification data set from MPEG Compact Descriptor for Visual Search (CDVS) effort demonstrate the effectiveness of this approach.