Efficient point cloud geometry compression (PCGC) is essential for 3D sensing applications such as autonomous driving and digital twins. Hybrid methods combine the generalization of learned models with the adaptability of Implicit Neural Representations (INRs), but existing schemes often use complex architectures, rigid update strategies, and post-training weight compression, limiting adaptation efficiency and increasing bitstream overhead. This paper proposes Hybrid-PCGC, which formulates hybrid compression as a joint geometry-and-model bitrate allocation problem and optimizes instance-specific weight offsets as rate-constrained transmitted variables. It separates dataset-agnostic prior modeling from instance-specific adaptation through four components: a Frozen General Module (FGM), a Dual Trainable Adaptive Module (DTAM), an Adaptive Scope Controller (ASC), and a Supervised Offset Compression Module (SOCM). FGM captures global geometric regularities, while the parameter-efficient DTAM adapts to individual point clouds. ASC selects the update scope based on coding difficulty, and SOCM jointly optimizes and compresses weight offsets through differentiable entropy supervision during training. Experiments show that Hybrid-PCGC balances fidelity and bitrate, reducing bitrate by 13.96% and 16.33% over G-PCC on lossless and lossy benchmarks, respectively, and saving up to 12.33% bitrate over the state-of-the-art hybrid method on challenging dense point clouds.
Figures & tables
Fig. 1 : Principle comparison of generalization-based, INR-based, and our Hybrid approaches, highlighting the model components utilized during the pretrain, instance adaptation, and weight coding phases.
Fig. 2 : Concept in octree.
Fig. 3 : (a) The framework includes the Pipeline. (b) The architecture of the FGM, which iteratively generates a robust prior. (c) The architecture of the DTAM, which consumes the prior from the FGM to refine the final occupancy prediction Pocci+1,j .
Fig. 4 : Component of FGM. (a) FGM and ResNet. (b) Fusion module. It is used to integrate global information with the decoded local information. (c) Gate. It employs learnable weighting coefficients M1 and M2 to control the weighted sum of the two input features.
Fig. 5 : Principle of SOCM, where selected offset weights are quantized through STE and entropy-regularized by an entropy bottleneck. AE is an arithmetic encoder, AD is an arithmetic decoder.
Dataset
SparsePCGC
Unicorn
OctAttention
TopNet
GPCC
AnyPCC
AnyPCC-U
Ours
Ours-U
8iVFB
0.57
0.57
0.68
0.59
0.76
0.54
0.57
0.52
0.54
MVUB
0.69
0.69
0.76
0.69
0.94
0.67
0.75
0.65
0.70
Owlii
0.48
0.48
0.62
0.56
0.59
0.47
0.47
0.45
0.44
Thuman
1.70
1.70
2.31
2.20
2.00
1.58
1.64
1.56
1.59
ScanNet
1.86
1.86
2.13
2.03
2.03
1.83
1.88
1.78
1.81
KITTI
6.80
6.50
7.21
6.85
8.19
6.18
6.45
6.03
6.35
TABLE I: Lossless geometry compression performance on the benchmark. Bpp is reported for each dataset, with the best value in each row highlighted with a red background and the second-best value highlighted with a blue background. CR Gain is computed relative to G-PCC, and Enc/Dec denotes average encoding/decoding time. Ours and Ours-U correspond to the dataset-specific and unified settings, respectively. The results for Unicorn are cited directly from the original publication.
Fig. 6 : Performance comparison using rate-distortion curves. Comparisons on the human-body and ScanNet datasets exclude RENO, OctAttention, and TopNet, as they are specifically designed for lossy LiDAR compression and thus incompatible.
Dataset
UniPCGC
AnyPCC
AnyPCC-U
Ours
Ours-U
8iVFB
2.96%
-15.01%
-6.74%
-19.00%
-13.43%
MVUB
3.64%
-10.49%
0.04%
-16.14%
-4.29%
Owlii
1.50%
-20.45%
-10.72%
-19.26%
-13.96%
Thuman
-16.88%
-50.41%
-40.43%
-52.23%
-44.04%
ScanNet
/
-75.88%
-74.88%
-75.54%
-74.89%
Avg.
-2.20%
-34.45%
-26.54%
-36.44%
-30.12%
TABLE II : BD-Rate comparison on dense object and indoor point clouds using SparsePCGC as the anchor. Negative values indicate bitrate savings over SparsePCGC, and lower values are better.
Dataset
RENO
TopNet
AnyPCC
AnyPCC-U
Ours
Ours-U
KITTI
-12.31%
-16.81%
-23.37%
-20.07%
-26.83%
-23.19%
Ford
-8.84%
-17.55%
-18.60%
-17.47%
-21.72%
-20.64%
VGGT
9.75%
4.77%
-0.42%
-4.88%
-0.43%
-6.31%
Avg.
-3.80%
-9.86%
-14.13%
-14.14%
-16.33 %
-16.72%
TABLE III : BD-Rate comparison on LiDAR and reconstruction-derived point clouds using G-PCCv23 as the anchor. Negative values indicate bitrate savings over G-PCCv23, and lower values are better.
Ours-U
Ours-U.M
CR-Gain
Ford
8.57
8.51
-0.70%
Dense
5.55
5.45
-1.80%
RS
3.50
3.4
-2.86%
NS
4.69
4.62
-1.49%
CS
3.18
2.96
-6.92%
Avg.
5.098
4.988
-2.16%
TABLE IV : Effect of using M>1 point clouds in one adaptation unit. Ours-U denotes the unified model with M=1 , while Ours-U.M shares one adapted offset among multiple point clouds.
Dataset
G-PCC
AnyPCC-U.B
Ours-U.B
8iVFB
0.57
0.56
0.54
Ford
9.77
8.57
8.55
GS
15.82
13.29
13.27
CS
4.94
3.36
3.28
Avg.
7.78
6.45
6.41
CR-Gain
0.00%
-17.11%
-17.56%
TABLE V : Comparison of unified base models without instance adaptation. AnyPCC-U.B and Ours-U.B denote the corresponding base models before optimizing instance-specific weight offsets.
Fig. 7 : Distribution evolution of transmitted weight offsets on CS during online instance adaptation. The blue histogram denotes the empirical frequency of quantized offsets, and the orange curve denotes the PMF estimated by SOCM.
Fig. 8 : Bitrate allocation across geometry scales and transmitted model offsets on representative datasets.
IA
SOCM
ASC
Gain vs. Abla.3
Ours-U
✓
✓
✓
-7.21%
Abla.1
✓
✓
✗
-5.93%
Abla.2
✓
✗
✗
-5.66%
Abla.3
✗
✗
✗
0.00%
TABLE VI : Component ablation of the proposed unified model. IA denotes online instance adaptation. Gain is computed within this ablation study using Abla.3 as the reference.
Dataset
Ours-U
Abla.1
Abla.2
Abla.3
8iVFB
0.54
0.56
0.54
0.54
Ford
8.54
8.66
8.55
8.55
GS
11.7
11.7
12.14
13.27
CS
3.00
3.00
3.10
3.61
Gain vs. Abla.3
-7.21%
-5.93%
-5.66%
0.00%
TABLE VII : Detailed bitrate results for the ablation study. Gain is computed over the four representative datasets using Abla.3 as the reference.
Symbol
Description
Value
CFGM
Network channels used for FGM prior modeling in Stage I
64
KFGM
Convolution kernel size used for FGM prior modeling in Stage I
5
CDTAM
Network channels used for DTAM refinement in Stage II
8
KDTAM
Convolution kernel size used for DTAM refinement in Stage II
5
B
Batch size
1
lroff
Initial learning rate for offline training
0.0005
TABLE VIII: Training and online instance-adaptation parameters used in our experiments. Stage I, Stage II, and online instance adaptation correspond to the three-step training strategy described in the main paper. Dataset-dependent online settings follow the lossless testing protocol described in the main paper.
Dataset
Depth
Frames/Seq.
Total
Cond.
Avg. Points
Sequence/Source
8iVFB
10-bit
300
600
Easy
760375
Loot, Redandblack
MVUB
10-bit
/
461
Easy
1259792
Phil, Ricardo
Owlii
11-bit
600
2400
Easy
2635971
All sequences
Thuman
10-bit
/
20
Easy
441319
Frames 0500–0519
ScanNet
2 cm
100
100
Easy
126760
ScanNet test split
KITTI
1 mm
100
1100
Easy
119826
Sequences 11–21
TABLE IX : Test dataset protocol inherited from the AnyPCC benchmark. “Total” denotes the number of tested point clouds/frames. “/” means that the dataset is not organized by a fixed frames-per-sequence value in the benchmark table. Average point counts are reported before entropy coding under the benchmark preprocessing.
Checkpoint
Training data/source
Test use in Ours
8iVFB
8iVFB Longdress/Soldier and MVUB Andrew/David/Sarah training sequences
8iVFB Loot/Redandblack, MVUB Phil/Ricardo, and Owlii sequences
Thuman
Thuman training split, frames 0000–0499
Thuman test frames 0500–0519
ScanNet
ScanNet benchmark training scans with the same preprocessing
Disjoint ScanNet benchmark test split
KITTI
SemanticKITTI sequences 00–10, quantized from 1 mm coordinates
KITTI sequences 11–21 and the benchmark OOD sets mapped to the LiDAR-style checkpoint
Ford
Ford 01 training sequence with 1 mm quantization
Ford 02–03 test sequences
GS
Gaussian-splatting-derived training point clouds from the benchmark training source
Disjoint GS test set
TABLE X : Category-specific training and checkpoint selection used by Ours. Ours-U replaces all category-specific checkpoints in this table with one unified checkpoint.
Scope
Updated DTAM components
Offset params
l=−1
No instance adaptation; no DTAM offset is transmitted
0
l=0
Final linear layer of the occupancy-prediction MLP
528
l=1
MLPs involved in each DTAM stage
25,616
l=2
Stage-wise MLPs and their preceding SConv layers
31,008
l=3
Entire DTAM parameter set
44,952
TABLE XI: Definition of ASC adaptation scopes.
Field
Type
Length
Decoding dependency
Scope flag l
uint8
1 byte
Selects the DTAM offset tensor list and NΔWl
EB parameter count
uint32
4 bytes
Sanity check against fixed NEB
EB parameters
uint8 array
NEB bytes
Dequantized by fixed per-tensor affine rules to compute the CDF
exp(Fa)
float16
2 bytes
Inverse scale for decoded offset symbols
Offset payload size
uint32
4 bytes
Number of bytes in the arithmetic-coded offset stream
Offset payload
byte array
BAE bytes
Arithmetic decoded with the CDF computed from EB parameters
TABLE XII : Byte-level syntax of the model-offset sub-bitstream. The CDF is computed at the decoder from the transmitted entropy-bottleneck parameters and fixed codec constants; it is not transmitted as side information.
LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the compression speed and performance of existing methods. To address this challenge, we propose a compact representation for efficient predictive lossless coding. Our framework comprises two lightweight modules. First, the Geometry Re-Densification Module iteratively densifies encoded sparse geometry, extracts features at a dense scale, and then sparsifies the features for predictive coding. This module avoids costly computation on highly sparse details while maintaining a lightweight prediction head. Second, the Cross-scale Feature Propagation Module leverages occupancy cues from multiple resolution levels to guide hierarchical feature propagation, enabling information sharing across scales and reducing redundant feature extraction. Additionally, we introduce an integer-only inference pipeline to enable bit-exact cross-platform consistency, which avoids the entropy-coding collapse observed in existing neural compression methods and further accelerates coding. Experiments demonstrate competitive compression performance at real-time speed. Code will be released upon acceptance. Code is available at https://github.com/pengpeng-yu/FastPCC.
Pengpeng Yu, Haoran Li, Runqing Jiang +4
Sun Yat-sen University, China · Pengcheng Laboratory, China
Plenoptic point clouds (PPC) are novel data structures that represent the light from different viewing directions in order to provide a higher degree of realism to regular point clouds. This is achieved by associating each point to multiple colors instead of a single one. Here, we present a method to efficiently compress the attributes of a PPC, consisting of a Karhunen-Loève transform over the color attributes followed by multiple attribute coders with intra prediction capability. This compression scheme can be incorporated within the MPEG's geometry-based PCC (G-PCC) standard, using any of G-PCC's existing solutions for attribute coding. Compression performance assessment using PPCs of different spatial resolutions reveals competitive results in comparison to existing methods, such as RAHT-based or video-based PCC solutions. We believe our coder to be the new state of the art.
Davi R. Freitas, Gustavo L. Sandri, Ricardo L. de Queiroz
Inria - Rennes Bretagne-Atlantique Rennes, France · Instituto Federal de Bras´ılia Bras´ılia, Brazil · Universidade de Bras´ılia Bras´ılia, Brazil
Octree-based context learning has recently become a leading method in point cloud compression. However, its potential on lossy compression remains undiscovered. The traditional lossy compression paradigm using lossless octree representation with quantization step adjustment may result in severe distortions due to massive missing points in quantization. Therefore, we analyze data characteristics of different point clouds and propose lossy approaches specifically. For object point clouds that suffer from quantization step adjustment, we propose a new leaf nodes lossy compression method, which achieves lossy compression by performing bit-wise coding and binary prediction on leaf nodes. For LiDAR point clouds, we explore variable rate approaches and propose a simple but effective rate control method. Experimental results demonstrate that the proposed leaf nodes lossy compression method significantly outperforms the previous octree-based method on object point clouds, and the proposed rate control method achieves about 1% bit error without finetuning on LiDAR point clouds.
Kaiyu Zheng, Wei Gao, Huiming Zheng
School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China