Temporal-Guided DPC Compression

• Temporal-guided DPC is an advanced compression framework that models inter-frame dependencies using hierarchical motion estimation and attention-based block matching.
• It employs coarse-to-fine motion estimation and KNN-attention to capture both global movement and local geometric changes in 3D dynamic point clouds.
• The approach achieves significant rate–distortion improvements over traditional methods, with up to 88.80% BD-rate gains on challenging datasets.

The Temporal-Guided DPC (Dynamic Point Cloud Compression) algorithm refers to advanced data-driven frameworks for compressing 3D dynamic point clouds by utilizing temporal context modeling, hierarchical motion estimation, and attention-based block matching modules. These frameworks address the challenges of dynamic point cloud sparsity and irregularity by extracting inter-frame dependencies and optimizing rate–distortion efficiency through learned geometric and feature correspondences.

1. Foundational Principles of Temporal-Guided DPC Algorithms

Temporal-guided DPC frameworks are designed to overcome the limited temporal modeling in traditional point cloud compression schemes, which often fail to capture frame-to-frame geometric correlations due to the non-uniformity and sparsity of dynamic point clouds. The central innovation lies in hierarchical motion estimation/motion compensation (Hie-ME/MC), which operates in the latent feature space at multiple resolutions. Coarse motion estimation (on low-resolution features) captures global movement, while subsequent refinement (on higher-resolution features) focuses on local geometric changes. This design enables the compression algorithm to flexibly control the granularity of optical flow used for inter-prediction, adapting to the motion characteristics within each temporal block.

2. Hierarchical Inter-Frame Block Matching and Motion Compensation

The Hie-ME/MC process, as detailed in (Xia et al., 2023), consists of:

• Coarse-level motion estimation: Motion vectors are estimated using features downsampled by a factor of 3. These vectors predominantly encode large-scale movement, analogous to coding units in conventional video codecs.
• Fine-level refinement: Features downsampled by a factor of 2 are used for precise corrections, capturing subtle local shifts.
• Channel-wise motion compensation: Latent features for each channel are independently warped using estimated optical flow:

$u_w^{(i)} = u + m_{t,u}^{(i)}$, $\forall u \in C(y_t^{(2\downarrow)})$

A subsequent 3D Adaptively Weighted Interpolation computes interpolated features by aggregating over the nearest neighbors in the warped reference space. The interpolation formula is:

$$\overline{y}_{t,u}^{(i)} = \frac{\sum_{v \in \theta(u_w^{(i)})} d(u_w^{(i)}, v)^{-1} y_{\mathrm{ref}, v}^{(i)}}{\max\left(\sum_{v \in \theta(u_w^{(i)})} d(u_w^{(i)}, v)^{-1}, \alpha\right)}$$

where $\theta(u_w^{(i)})$ denotes the set of the 3 nearest neighbors and $\alpha$ penalizes isolated points.

This structure allows block-wise matching and compensation at multiple spatial scales, enabling robust modeling of both macro and micro movements in the point cloud sequence.

3. KNN-Attention Block Matching (KABM) Network Architecture

A critical component in the temporal-guided paradigm is the KNN-attention block matching (KABM) module, which generalizes flow estimation beyond simplistic one-to-one correspondences. The procedure involves:

• Ball-KNN search to identify local neighborhoods within the concatenated latent features of reference and target frames.
• Calculation of geometric and feature-based measures for these neighborhoods.
• Application of inter-frame attention networks, yielding soft weights for each potential correspondence.
• Aggregation of neighbor attributes using a weighted sum followed by a multi-layer perceptron (MLP), producing flow embedding vectors robust to geometric and semantic variability across frames.

The soft aggregation mechanism explicitly models uncertainty in correspondences and is particularly beneficial for handling noisy or sparsely sampled regions common in dynamic point clouds.

4. Compression and Entropy Modeling in Latent Space

After hierarchical inter-prediction, the residuals (difference between predicted and true latent features) are encoded via auto-encoder structures. Both residual and multi-scale optical flow embeddings undergo quantization and are compressed through a fully factorized deep entropy model, wherein each latent feature is assumed independent and parameterized by learnable parameters $\psi$:

$$R_F = \frac{1}{N} \sum_i -\log_2(P(\tilde{F}_i | \psi^{(i)}))$$

This probabilistic modeling approach enables the encoder to align bit allocation with true data distributions, maximizing compression efficiency, and adapting dynamically to the temporal and spatial patterns in the input stream.

5. Empirical Benchmarks and Performance Gains

On the MPEG Owlii dataset, temporal-guided DPC algorithms demonstrate marked improvements over traditional and prior deep-learning compression schemes. Notably:

• Average BD-rate savings of 9.96% against D-DPCC (a prior deep point cloud codec) measured using point-to-point (D1) distortion metrics.
• Up to 88.80% BD-rate improvement over the MPEG V-PCC v18 standard in inter-frame low-delay compression mode.
• Particular efficacy is observed for sequences characterized by low-amplitude movements, as the system’s fine-grained motion estimation mitigates artifacts and reduces reconstruction error.

The superior performance is attributable to effective temporal mining and adaptive granularity selection in motion modeling, as well as more discriminative block correspondence via KABM.

6. Methodological Implications and Practical Considerations

Temporal-guided DPC algorithms present a flexible, adaptive framework for high-efficiency dynamic point cloud compression. Key implications for practical deployments include:

• Selection of optical flow granularity may be dynamically tuned according to motion and coding rate constraints, enabling unified handling of diverse scenes.
• KABM’s explicit modeling of geometric and feature correlations facilitates robust compression even under severe point sparsity.
• The use of a fully factorized deep entropy model supports real-time bit allocation and streaming applications, though potential limitations may arise in extremely low-density or highly irregular point clouds.
• Hardware acceleration, memory bandwidth, and parallelization should be considered when deploying hierarchical motion estimation and attention mechanisms at scale.

These algorithmic advancements are foundational for next-generation 3D video codecs and are well-suited for applications involving immersive telepresence, autonomous sensing, and volumetric video.

7. Relationship to Broader Temporal Modeling Techniques

Temporal-guided DPC shares conceptual foundations with methods in video compression (rate-distortion optimization, block-based motion compensation) and graph-based signal processing but adapts them to the specific constraints and opportunities posed by point cloud data. The temporal mining, block-matching, and entropy coding approaches described in (Xia et al., 2023) have broader significance for any domain in which spatiotemporal coherence must be efficiently modeled and exploited under sparsity and non-uniformity.

In summary, Temporal-Guided DPC algorithms represent a substantial technical advancement in dynamic point cloud compression, combining hierarchical temporal modeling, attention-driven block matching, and probabilistic entropy coding to achieve superior compression ratios and reconstruction fidelity relative to prior art. These techniques are directly applicable to demanding real-time scenarios in streaming 3D data and establish methodological benchmarks for future developments in the field.