SEDD-PCC
A Single Encoder－Dual Decoder Framework For End-To-End Learned Point Cloud Compression

Kai-Hsiang Hsieh, Monyneath Yim, Jui-Chiu Chiang,
National Chung Cheng University, Taiwan
Paper arXiv Code
Teaser

Fig. 1 Existing Learning-based point cloud compression solutions, (a) Geometry compression. (b) Attribute compression.
(c) Joint geometry and attribute compression through recoloring.
(d) Proposed SEDD-PCC: an end-to-end learned point cloud compression scheme.

Overview

🔍Problem: Most learning-based compression schemes treat geometry and attribute coding separately, employing distinct encoders and decoders. This not only increases computational complexity but also fails to fully exploit shared features between geometry and attributes.

✅Our Solution: To address this limitation, we propose SEDD-PCC, an end-to-end learning-based framework for lossy point cloud compression that jointly compresses geometry and attributes. SEDD-PCC employs a single encoder to extract shared geometric and attribute features into a unified latent space, followed by dual specialized decoders that sequentially reconstruct geometry and attributes.

🎯Enhancement: We incorporate knowledge distillation to enhance feature representation learning from a teacher model, further improving coding efficiency. With its simple yet effective design, SEDD-PCC provides an efficient and practical solution for point cloud compression.

📊Results: Comparative evaluations against both rule-based and learning-based methods demonstrate its competitive performance, highlighting SEDD-PCC as a promising AI-driven compression approach.

Method

Three-stage architecture
Figure 2: The three-stage training strategy.
Stage 1: Attribute compression ; Stage 2: Geometry compression, adopting a teacher model for knowledge distillation ;
Stage 3: Fine-tune all components by initializing Encoder_U and Decoder_A from Stage 1, and Decoder_G from Stage 2.

Network Architecture

Three-stage architecture
Figure 3: The proposed SEDD-PCC architecture.
The proposed SEDD-PCC architecture, illustrated in Fig. 3, consists of a single encoder
and dual decoders designed for joint compressing of geometry and attributes.

Experiment results

To comprehensively evaluate the performance of our SEDD-PCC, we compared it against standard MPEG benchmarks, including G-PCC TMC13 v23 Octree-RAHT and V-PCC TMC2 v22, following the MPEG Common Test Condition (CTC).

Additionally, we compare our approach with several learning-based techniques for joint point cloud compression, specifically YOGA, DeepPCC, Unicorn, and JPEG Pleno.

For a more comprehensive comparison, Table 1 presents the BD-BR (%) of our method and various benchmarks, using G-PCC as the anchor..

Notably, our approach achieves substantial bitrate reductions compared to G-PCC, with average savings of 75.0% in D1-PSNR, 32.6% in Y-PSNR, and 33.2% in 1-PCQM.

Furthermore, SEDD-PCC demonstrates superior coding performance when compared with JPEG Pleno for the Soldier sequence. Our method also shows a higher BD-rate saving in terms of 1-PCQM when compared to YOGA and DeepPCC, for the 11-bit sequences.

Fig 1 Fig 2 Fig 3 Fig 4 Fig 5 Fig 6

Fig. 4 R-D performance of the proposed scheme in terms of 1-PCQM.

Table 1. BD-rate (%) against G-PCCv23 for various schemes

BD-rate comparison

Complexity Evaluation

Complex

Table 2 presents the complexity analysis in terms of model size and encoding/decoding time. Due to availability constraints, only a subset of learning-based joint coding schemes is included.

Our SEDD-PCC has a model size of 32.6 MB, making it significantly lighter than other methods. Its encoding and decoding process is exceptionally fast.

Additionally, it eliminates the need for bit allocation between geometry and attribute coding for each point cloud, as well as recoloring, thereby reducing overall processing time.

BibTeX

@misc{hsieh2025seddpcc,
  title     = {SEDD-PCC: A Single Encoder--Dual Decoder Framework for End-to-End Learned Point Cloud Compression},
  author    = {Kai-Hsiang Hsieh and Monyneath Yim and Jui-Chiu Chiang},
  year      = {2025},
  eprint={2505.16709},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2505.16709}
}