ProDAT: Progressive Point Cloud Coding

• ProDAT is a density-aware progressive coding framework that leverages deep autoencoders and a tailored tail-drop operator to preserve high-detail regions in 3D point clouds.
• It employs a scalable, rate-adaptive decoding strategy with a user-controlled progressive ratio, achieving significant rate–distortion improvements on real-world benchmarks.
• The method supports real-time applications in autonomous driving, AR/VR, and immersive telepresence by balancing rapid coarse previews with progressive detail refinement.

ProDAT (Progressive Density-Aware Tail-Drop) is a novel learning-based framework for progressive point cloud coding, engineered to address the challenges of transmitting and reconstructing large-scale 3D point clouds under stringent bandwidth and latency constraints. The method enables scalable, rate-adaptive geometry compression and decoding using a density-sensitive mechanism that prioritizes the retention of information in regions of high geometric detail. ProDAT achieves significant improvements in rate–distortion performance over previous coding methods while supporting progressive (multi-level-of-detail) reconstruction from a single model.

1. Motivation and Context

Point clouds are extensively used in autonomous driving, augmented/virtual reality, and immersive communication, and pose significant coding and transmission challenges due to their irregular, non-grid structure and high data volumes. Conventional codecs designed for images and videos cannot efficiently preserve the fine-scale properties essential for faithful geometry reconstruction. Recent learning-based coding approaches, despite their success in geometric fidelity, lack support for progressive decoding since their latent representations are monolithic and fixed in dimensionality. ProDAT bridges this gap by introducing a mechanism that enables incremental, detail-refining decoding—essential for real-time, quality-adaptive applications—using density information as a guidance signal from within the scene.

2. Architecture and Mechanism

The ProDAT architecture is based on a deep autoencoder, with key extensions that enable density-aware progressive coding:

• Feature Extraction and Downsampling: An initial feature extractor $F$ maps the input 3D coordinates $X \in \mathbb{R}^{3 \times N}$ to a higher-dimensional feature space. The encoder $E$ then downsamples the point cloud to generate latent features $z$, downsampled coordinates $z_{xyz}$, and local density statistics $d$.
• Density-Aware Tail-Drop Operator: Channel retention is governed by a composite density score $\delta$ for each downsampled point, determined by both cluster size and local spatial dispersion. Let $d_{num}$ be the count of original points mapped to the downsampled location and $d_{dist}$ be their aggregated Euclidean distance. These are normalized by $d_{max}$ and $m_{max}$, dynamically adjusted via Exponential Moving Average:

$$\delta = \frac{1}{2} \left( \frac{d_{num}}{d_{max}} + \left[1 - \frac{d_{dist}}{m_{max}}\right] \right)$$

The channel drop ratio $\rho$ is set in $[\rho_{min}, \rho_{max}]$ (typically $0.15$ to $0.40$):

$$\rho = \rho_{max} - (\rho_{max} - \rho_{min}) \cdot \delta$$

Channel importance $I_c$ combines normalized global channel variance and local gradient magnitude with weighting $\beta=0.6$:

$$I_c = \beta \cdot \text{norm}(\text{Var}_c) + (1-\beta) \cdot \text{norm}(\text{Grad}_c)$$

A binary mask $M(\cdot,\rho)$ retains the highest-importance $(1-\rho)$ fraction of channels for both features and coordinates, crucially preserving geometric correlations.

• End-to-End Coding Flow:

F: X ⟶ F(X) [z, z_xyz, d] = E(X, F(X)) [z_ρ, z_xyz,ρ] = T_ρ(z, z_xyz, d) X'_ρ = D(A_z(z_ρ), A_xyz(z_xyz,ρ))

Here, $T_ρ$ represents density-aware tail-drop, $A_z$/$A_{xyz}$ are entropy bottleneck quantizers, and $D$ is the decoder.

3. Progressive Decoding Strategy

ProDAT enables progressive reconstruction through a user-controlled Progressive Ratio (PR), $\alpha = 1 - \rho$, specifying the fraction of latent channels activated during decoding. At low PR (many channels dropped), the network reconstructs a coarse, structurally salient approximation; as PR increases, further details are incrementally revealed, with channel selection guided by the density scores. High-density regions consistently retain more channels, ensuring that geometric nuances are preserved, and progressive quality improvement is smoothly achieved. This mechanism allows a single trained model to support multiple bitrates and levels of detail, critical for streaming and resource-adaptive deployments.

4. Experimental Evaluation

Evaluations are conducted on SemanticKITTI (real-world LiDAR scans of urban environments) and ShapeNet (synthetic 3D object models):

• Implementation Details: Encoder stages with downsampling ratios of $1/2$, $1/3$, $1/4$; decoder upsamples up to $\times 8$. The number of latent channels increases from 8 to 32 to provide diversity for effective progressive decoding.
• Loss Formulation: Optimization targets geometric fidelity (Chamfer Distance loss $\mathcal{L}_{CD}$), density ($\mathcal{L}_{Dens}$), coordinate ($\mathcal{L}_{Coord}$), and point ($\mathcal{L}_{Points}$) with bitrate penalty ($\mathcal{R}_{BPP}$):

$$\mathcal{L} = \mathcal{L}_{CD} + \sigma \mathcal{L}_{Dens} + \omega \mathcal{L}_{Coord} + \eta \mathcal{L}_{Points} + \lambda \mathcal{R}_{BPP}$$

• Results: On SemanticKITTI, ProDAT achieves over $28.6\%$ BD-rate improvement (PSNR-D2) versus D-PCC baseline; on ShapeNet, over $18.15\%$ improvement is reported. BPP vs. PSNR/Chamfer plots confirm substantial quality gains at low bitrates. Visualizations demonstrate that essential scene geometry is preserved even at low PR (e.g., $PR=0.03$), with fidelity scaling up as more latent channels are decoded.

5. Applications and Real-World Implications

ProDAT’s progressive and density-aided coding offers distinct advantages in domains where scalability, latency, and resource constraints are central:

• Autonomous Driving: Enables rapid coarse geometry reconstruction for real-time perception from LiDAR, with subsequent refinements assimilated as computational/hardware capacity allows.
• Augmented/Virtual Reality (AR/VR): Facilitates streaming workflows where a low-detail initial scene is rendered for fast interaction, and higher detail is loaded as needed for immersive experiences.
• Immersive Telepresence/Remote Sensing: Supports real-time communication and collaboration on 3D environments, balancing quick preview delivery and progressive enhancement.

A plausible implication is that such density-sensitive progressive coding could be integrated into broader perception and transmission pipelines for intelligent autonomous systems, maximizing resource utilization and user experience.

6. Conclusion and Prospective Research

ProDAT presents an end-to-end framework for progressive, density-aware point cloud coding, overcoming limitations of prior one-shot latent representations. The method demonstrates substantial advances in rate–distortion efficiency (28.6% BD-rate gain on SemanticKITTI; 18.15% on ShapeNet) and supports flexible, bitstream-controlled geometry refinement. Future research directions include refinement of density scoring and channel selection heuristics, investigation of alternative losses for perceptual quality, and extension to multimodal point cloud attributes (such as color or reflectivity). Adaptation to streaming environments and integration with real-time end-to-end perception architectures constitute promising further avenues.