Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure

Video-based point cloud compression (V-PCC) has achieved remarkable performance in dynamic point cloud compression. However, due to the 3D-to-2D projection in V-PCC, the temporal continuity across projected video frames is broken, limiting inter-frame coding efficiency. In this paper, we propose a virtual reference-based predictive coding (VRPC) scheme for V-PCC attribute compression to improve coding efficiency. This coding scheme uses the temporal correspondence between point clouds to guide the alignment of attribute video frames for virtual reference frame generation. Specifically, we first construct a neural deformation field-based geometry alignment module to align the reference and target point clouds to reduce the effect of motion on temporal correspondence estimation. Then, temporal correspondence is obtained in 3D space and used to align the reference and target video frames to generate the occupied region of the virtual reference frame. Finally, after padding the unoccupied region, the resulting reference frame is added to the reference picture list and used by the target frame for inter-frame coding. Experimental results demonstrate that the proposed method effectively improves the attribute coding efficiency of V-PCC.

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame's feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group.

In recent years, 3D point cloud content has gained attention due to its application possibilities, such as multimedia systems, virtual, augmented, and mixed reality, through the mapping and visualization of environments and/or 3D objects, real-time immersive communications, and autonomous driving systems. However, raw point clouds demand a large amount of data for their representation, and compression is mandatory to allow efficient transmission and storage. The MPEG group proposed the Video-based Point Cloud Compression (V-PCC) standard, which is a dynamic point cloud encoder based on the use of video encoders through projections into 2D space. However, V-PCC demands a high computational cost, demanding fast implementations for real-time processing and, especially, for mobile device applications. In this paper, a machine-learning-based fast implementation of V-PCC is proposed, where the main approach is the use of trained decision trees to speed up the block partitioning process during the point cloud compression. The results show that the proposed fast V-PCC solution is able to achieve an encoding time reduction of 42.73% for the geometry video sub-stream and 55.3% for the attribute video sub-stream, with a minimal impact on bitrate and objective quality.

Point clouds are a versatile 3D data representation format that captures spatial and attribute information about objects or environments. It has been widely used in applications like autonomous driving, augmented and virtual reality, and 3D scanning. However, the large volume of data involved in point cloud processing poses challenges in terms of storage, transmission, and real-time processing. The Video-based Point Cloud Compression (V-PCC) standard addresses these challenges by employing 2D video compression techniques to encode dynamic 3D point clouds. Despite its effectiveness, V-PCC demands a high computational cost, particularly in encoding geometry and attribute video sub-streams. This paper presents a machine learning-based approach to accelerate the V-PCC encoding, focusing on the Coding Unit partitioning process in the geometry and attribute sub-streams deployed in the reference software (TMC2). The proposed method significantly reduces the encoding complexity by using decision tree models with negligible impact on coding efficiency. Experimental results demonstrate an average encoding time reduction of 36.12% for geometry and attribute sub-streams, with a minimal impact on coding efficiency between -0.43% and 0.83% in terms of BD-Rate.

Point cloud compression has been studied in standard bodies and we are here concerned with the Moving Picture Experts Group video-based point cloud compression (V-PCC) solution. Plenoptic point clouds (PPC) is a novel volumetric data representation wherein points are associated with colors in all viewing directions to improve realism. It is sampled as a number ( Nc ) of attribute colors per point. We propose a new method for the efficient video-based compression of PPC that is backwards compatible with the existing single-color V-PCC decoder. V-PCC generates three image atlases which are encoded using an image/video encoder. We assume there may be a reference color which is to be encoded as the main payload. We generate Nc+3 atlases and we produce Nc differential images against the reference color image. Those difference images are pixel-wise transformed using an Nc -point discrete cosine transform, generating Nc transformed atlases which are encoded, forming the secondary payload. Such secondary information is the plenoptic enhancement to the point cloud. If there is no reference attribute, we skip the differences and use the lowest frequency of the transformed atlases as the main payload. Results are presented that show an unrivaled performance of the proposed method.

We present a study on the impact of packet loss on dynamic 3D point cloud streaming, encoded with MPEG Video-based Point Cloud Compression (V-PCC) standard. We show the distortion when different channels of V-PCC bitstream are lost, with the loss of occupancy and geometry data impacting the quality most significantly. Our results point to the need for better error concealment techniques. We end the paper by presenting preliminary thoughts and experimental results of two naive error concealment techniques in the point cloud domain, for attributes and geometry data, respectively, and highlight the limitations of each.

Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. In this work, we propose a first point cloud interpolation framework for photorealistic dynamic point clouds. Given two consecutive dynamic point cloud frames, our framework aims to generate intermediate frame(s) between them. The proposed deep learning framework has three major components: the encoder module, the fusion network, and the multi-scale point cloud synthesis module. The encoder module extracts multi-scale features from two consecutive frames. The fusion network employs a novel 4D feature learning technique to merge the multi-scale features from consecutive frames. Finally, the multi-scale point cloud synthesis module hierarchically reconstructs the interpolated point cloud intermediate frame at different resolutions. We evaluate our framework on high-resolution point cloud datasets used in MPEG, JPEG Pleno, and AVS standards. The quantitative and qualitative results demonstrate the effectiveness of the proposed method.

Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs edge position difference oriented motion model. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 3.15% over the V-PCC HEVC reference encoder.

Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40× to 90× compression rate, significantly higher than the MPEG’s LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.

With the maturity of 3D capture technology, the explosive growth of point cloud data has burdened the storage and transmission process. Traditional hybrid point cloud compression (PCC) tools relying on handcrafted priors have limited compression performance and are increasingly weak in addressing the burden induced by data growth. Recently, deep learning-based PCC methods have been introduced to continue to push the PCC performance boundary. With the thriving of deep PCC, the community urgently demands a systematic overview to conclude the past progress and present future research directions. In this paper, we have a detailed review that covers popular point cloud datasets, algorithm evolution, benchmarking analysis, and future trends. Concretely, we first introduce several widely-used PCC datasets according to their major properties. Then the algorithm evolution of existing studies on deep PCC, including lossy ones and lossless ones proposed for various point cloud types, is reviewed. Apart from academic studies, we also investigate the development of relevant international standards (i.e., MPEG standards and JPEG standards). To help have an in-depth understanding of the advance of deep PCC, we select a representative set of methods and conduct extensive experiments on multiple datasets. Comprehensive benchmarking comparisons and analysis reveal the pros and cons of previous methods. Finally, based on the profound analysis, we highlight the challenges and future trends of deep learning-based PCC, paving the way for further study.

This paper presents a novel method to determine rate-distortion optimized\ntransform coefficients for efficient compression of videos generated from point\nclouds. The method exploits a generalized frequency selective extrapolation\napproach that iteratively determines rate-distortion-optimized coefficients for\nall basis functions of two-dimensional discrete cosine and sine transforms. The\nmethod is applied to blocks containing both occupied and unoccupied pixels in\nvideo based point cloud compression for HEVC encoding. In the proposed\nalgorithm, only the values of the transform coefficients are changed such that\nresulting bit streams are compliant to the V-PCC standard. For all-intra coded\npoint clouds, bitrate savings of more than 4% for geometry and more than 6% for\ntexture error metrics with respect to standard encoding can be observed. These\nsavings are more than twice as high as savings obtained using competing methods\nfrom literature. In the randomaccess case, our proposed method outperforms\ncompeting V-PCC methods by more than 0.5%.\n

Video-based Point Cloud Compression (V-PCC) standard offers state-of-the-art tools and efficiency for volumetric video compression. The V-PCC reference software, TMC2, is able to demonstrate the compression efficiency of V-PCC, but with computational complexity that is impractical for real-life applications. This paper introduces a novel open-source V-PCC encoder, named uvgVPCCenc, that brings V-PCC compression speed to a practical level. To reduce computational bottlenecks, uvgVPCCenc is implemented from the ground up in C++ with advanced multi-threading and streamlined algorithms. It also supports a flexible parameterization and different presets that facilitate its adaptation to various content and target bitrates. Our comparative evaluations show that uvgVPCCenc achieves substantial speedups over TMC2 while maintaining competitive rate-distortion-complexity tradeoff. To the best of our knowledge, uvgVPCCenc is the first open-source encoder explicitly designed for practical V-PCC compression, paving the way from theoretical research to practical volumetric video applications.

In this work, we propose a new sequential point cloud upsampling method called SPU, which aims to upsample sparse, non-uniform, and orderless point cloud sequences by effectively exploiting rich and complementary temporal dependency from multiple inputs. Specifically, these inputs include a set of multi-scale short-term features from the 3D points in three consecutive frames (i.e., the previous/current/subsequent frame) and a long-term latent representation accumulated throughout the point cloud sequence. Considering that these temporal clues are not well aligned in the coordinate space, we propose a new temporal alignment module (TAM) based on the cross-attention mechanism to transform each individual feature into the feature space of the current frame. We also propose a new gating mechanism to learn the optimal weights for these transformed features, based on which the transformed features can be effectively aggregated as the final fused feature. The fused feature can be readily fed into the existing single frame-based point cloud upsampling methods (e.g., PU-Net, MPU and PU-GAN) to generate the dense point cloud for the current frame. Comprehensive experiments on three benchmark datasets DYNA, COMA, and MSR Action3D demonstrate the effectiveness of our method for upsampling point cloud sequences.

3D point cloud presentation has been widely used in computer vision, automatic driving, augmented reality, smart cities and virtual reality. 3D point cloud compression method with higher compression ratio and tiny loss is the key to improve data transportation efficiency. In this paper, we propose a new 3D point cloud geometry compression method based on deep learning, also an auto-encoder performing better than other networks in detail reconstruction. It can reach much higher compression ratio than the state-of-art while keeping tolerable loss. It also supports parallel compressing multiple models by GPU, which can improve processing efficiency greatly. The compression process is composed of two parts. Firstly, Raw data is compressed into codeword by extracting feature of raw model with encoder. Then, the codeword is further compressed with sparse coding. Decompression process is implemented in reverse order. Codeword is recovered and fed into decoder to reconstruct point cloud. Detail reconstruction ability is improved by a hierarchical structure in our decoder. Latter outputs are grown from former fuzzier outputs. In this way, details are added to former output by latter layers step by step to make a more precise prediction. We compare our method with PCL compression and Draco compression on ShapeNet40 part dataset. Our method may be the first deep learning-based point cloud compression algorithm. The experiments demonstrate it is superior to former common compression algorithms with large compression ratio, which can also reserve original shapes with tiny loss.

Since the development of 3D applications, the point cloud, as a spatial description easily acquired by sensors, has been widely used in multiple areas such as SLAM and 3D reconstruction. Point Cloud Compression (PCC) has also attracted more attention as a primary step before point cloud transferring and saving, where the geometry compression is an important component of PCC to compress the points geometrical structures. However, existing non-learning-based geometry compression methods are often limited by manually pre-defined compression rules. Though learning-based compression methods can significantly improve the algorithm performances by learning compression rules from data, they still have some defects. Voxel-based compression networks introduce precision errors due to the voxelized operations, while point-based methods may have relatively weak robustness and are mainly designed for sparse point clouds. In this work, we propose a novel learning-based point cloud compression framework named 3D Point Cloud Geometry Quantiation Compression Network (3QNet), which overcomes the robustness limitation of existing point-based methods and can handle dense points. By learning a codebook including common structural features from simple and sparse shapes, 3QNet can efficiently deal with multiple kinds of point clouds. According to experiments on object models, indoor scenes, and outdoor scans, 3QNet can achieve better compression performances than many representative methods.