With the rapid advancement of Augmented Reality (AR) and autonomous driving, the storage and transmission of high-density point clouds have become critical challenges. To address this, we propose HMD-PCGC, a novel end-to-end Point Cloud Geometry Compression framework that synergizes a Mamba-Diffusion architecture with an adaptive hybrid representation learning mechanism. Unlike traditional approaches that rely on single-modality inputs, our encoder integrates three complementary feature types—point-based, voxel-based, and implicit features—through a Cross-Representation Attention module. This design allows for the comprehensive capture of local geometric details and complex topologies. To effectively model long-range dependencies within the latent space without the computational bottleneck of Transformers, we employ a Mamba block (State Space Model) for sequential processing, coupled with a scorer-guided adaptive quantization module that dynamically allocates bit-rate based on feature significance. For the decoding stage, we utilize a conditional Diffusion Model that generates high-fidelity point clouds by reversing a noising process, conditioned on the compressed latent representation. Extensive experiments on the MPEG 8iVFB and MVUB datasets demonstrate that HMD-PCGC achieves significant improvements in compression efficiency. Specifically, our method yields average BD-Rate gains exceeding 90% against G-PCC (octree), 80% against G-PCC (trisoup), and 40% against V-PCC, while also outperforming several recent learning-based frameworks in both rate-distortion efficiency and visual reconstruction quality.

Despite the efficiency of lossy geometry-based point cloud compression, decompressed point clouds exhibit distortions that depend on the compression level. Severe distortions, in particular, highlight the need for geometry restoration. Existing methods are limited in practice: they train separate models for specific distortion levels and require prior knowledge of these levels at inference, preventing generalization to broader distortion cases. To address these issues, we propose GGRNet, a generalizable geometry restoration network for compressed point clouds. GGRNet trains a single model and requires no prior knowledge of distortion levels for inference. Specifically, it adopts a sparse encoder-decoder for hierarchical geometry reconstruction. To handle diverse point densities and distortions, we introduce distortion-aware mixture-of-experts descriptors, which adaptively capture global and local features via dynamic gating. We further equip the decoder with distortion prompt adapters to implicitly encode and adapt to different distortion levels. Finally, we design a perceptual alignment loss to ensure perceptual invariance in the learned prompts. Extensive experiments show that GGRNet achieves strong performance in both the geometry restoration and downstream analysis tasks, demonstrating practical potential for real-world applications.

In this paper, we propose a deep hierarchical attention context model for lossless attribute compression of point clouds, leveraging a multi-resolution spatial structure and residual learning. A simple and effective Level of Detail (LoD) structure is introduced to yield a coarse-to-fine representation. To enhance efficiency, points within the same refinement level are encoded in parallel, sharing a common context point group. By hierarchically aggregating information from neighboring points, our attention model learns contextual dependencies across varying scales and densities, enabling comprehensive feature extraction. We also adopt normalization for position coordinates and attributes to achieve scale-invariant compression. Additionally, we segment the point cloud into multiple slices to facilitate parallel processing, further optimizing time complexity. Experimental results demonstrate that the proposed method offers better coding performance than the latest G-PCC for color and reflectance attributes while maintaining more efficient encoding and decoding run-times.

Dynamic point clouds serve as crucial representations of three-dimensional moving entities across diverse applications. The substantial amount of data in point clouds necessitates the development of efficient compression techniques. Motion estimation (ME) plays a crucial role in eliminating the temporal redundancy of point cloud sequences. However, prevailing ME methods suffer from the inaccurate geometry distortion measure and the imbalanced rate-distortion modeling, significantly impacting the coding performance. To address these challenges, we propose a rate-distortion (R-D) optimized ME scheme for dynamic point cloud geometry compression.

Learning-based lossy point cloud compression has garnered significant attention recently. However, current methods have insufficient exploration of entropy models, especially in the inter-group context. This paper proposes an innovative method called GroupAC for point cloud attribute compression by leveraging inter-group context. Initially, point cloud attributes are transformed into coefficients. We then develop a deep entropy model incorporating inter-group context to estimate the probability distribution of these coefficients. The deep entropy model splits the coefficients into groups, wherein the encoding groups utilize context from preceding groups to enhance probability distribution modeling. Finally, an arithmetic encoder compresses the coefficients into a bitstream based on the estimated probability distribution. Experimental results on indoor and outdoor point cloud datasets, including ScanNet and SemanticKITTI, demonstrate that our approach outperforms MPEG G-PCC (TMC13v23) and existing learning-based methods.

We present an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. It involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.

Existing point cloud place recognition methods ignore textureless descriptions of scenes by point clouds. This further leads to lower generalization and bottlenecks in performance improvement. To solve these problems, we propose a novel sketch-aided interactive fusion point cloud place recognition method, which involves two networks to separately deal with point clouds and sketches and an interaction feature fused module to fuse features mathematically. Specifically, this is the first time to introduce sketches to guide the point cloud place recognition task as far as we know. The sketch-aided part and the point cloud could enhance the texture structure of the scene which is omitted in only the point cloud scenario. Meanwhile, we devise an interactive feature fusion module for fusing two features, which is encouraged by square summation in math. This module reflects the communication between features as well as the non-linear influence on the fused feature without bringing dimension growth. The experiments on two datasets witness the effectiveness of the proposed method in performance improvement and generalization subjectively and objectively.

After the works on the state-of-the-art High Efficiency Video Coding (HEVC) standard, the standard organizations continued to study the potential video coding technologies for the next generation of video coding standard, named Versatile Video Coding (VVC). Transform is a key technique for compression efficiency, and core experiment 6 (CE6) is carried out to explore the transform related coding tools. In this paper, we propose a novel separable transform based on Karhunen-Loève Transform (KLT) to eliminate the horizontal and vertical correlations in the residual samples of intra coding. In the proposed method, the weaknesses of the traditional KLT are addressed. The separable KLT is developed as an alternative transform type in addition to DCT-II, and the transform matrices from 4×4 to 64×64 are trained from intra residual samples. Experimental results show the proposed method can achieve 2.7% bitrate saving averagely on top of the reference software of VVC (VTM-1.1), and the consistent performance improvement on test set also validates the strong generalization capacity of the proposed separable KLT.

Low-level image processing is mainly concerned with extracting descriptions (that are usually represented as images themselves) from images. With the rapid development of neural networks, many deep learning-based low-level image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning based approach for low-level image processing, in particular, image denoising, image deblurring, and compressed image restoration. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a multi-objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on three low-level image processing tasks on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against all current state-of-the-art approaches.

Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this article, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection.

Saliency detection has been an increasingly important tool for ROI selection in image compression. Most previous works on saliency detection are dedicated to conventional images, however, with the rapid development of VR or AR technology, it is becoming more and more important to obtain visual attention for panoramic images. Meanwhile, panoramic images have more potential for improvement in compression performance compared with the conventional case. In this work, we propose an innovative saliency guided ROI selection model. Extensive evaluations show the proposed approach outperforms other methods in saliency accuracy especially for panoramic images. Meanwhile, we improve the compression quality of standard JPEG by using a higher bit rate to encode image regions flagged by our model and lower bit rate elsewhere in the image.