REVIEW 10 cited by
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising image quality. Furthermore, subsequent studies have successfully extended 3DGS to dynamic 3D scenes, demonstrating its wide range of applications. However, a significant drawback arises as 3DGS and its following methods entail a substantial number of Gaussians to maintain the high fidelity of the rendered images, which requires a large amount of memory and storage. To address this critical issue, we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes, such as view-dependent color and covariance. To this end, we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally, we learn codebooks to compactly represent the geometric and temporal attributes by residual vector quantization. With model compression techniques such as quantization and entropy coding, we consistently show over 25x reduced storage and enhanced rendering speed compared to 3DGS for static scenes, while maintaining the quality of the scene representation. For dynamic scenes, our approach achieves more than 12x storage efficiency and retains a high-quality reconstruction compared to the existing state-of-the-art methods. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering. Our project page is available at https://maincold2.github.io/c3dgs/.
Forward citations
Cited by 10 Pith papers
-
GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens
GlobalSplat achieves competitive novel-view synthesis on RealEstate10K and ACID using only 16K Gaussians via global scene tokens and coarse-to-fine training, with a 4MB footprint and under 78ms inference.
-
Realizing Immersive Volumetric Video: A Multimodal Framework for 6-DoF VR Engagement
The paper presents a multimodal framework, dataset, and reconstruction pipeline to create immersive volumetric videos supporting large 6-DoF audiovisual interaction from real multi-view captures.
-
3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression
3DGS-VBench is a benchmark of 660 human-rated compressed 3D Gaussian Splatting models across 6 algorithms, with 15 quality metrics evaluated, for training 3DGS video quality assessment models.
-
3DGSI-Assessor: A Large-Scale Dataset and An LMM-based Method for 3D Gaussian Splatting Image Quality Assessment
A new 15,200-image human-annotated dataset and an LMM-based metric that jointly predicts overall, geometry, and color quality of compressed 3D Gaussian Splatting images.
-
SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM
SpatialQ combines a multi-view quality encoder with a Qwen-based MLLM that diagnoses degradation types and adjusts scores, reporting state-of-the-art correlation on 3DGS-IEval-15K.
-
PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
PD-4DGS decomposes 4DGS into static scaffold, global deformation, and local refinement layers using hierarchical decomposition and custom losses, achieving over 60% bitstream reduction and reducing first-frame latency...
-
GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
GS-NFS accelerates dynamic 3DGS encoding and decoding by 1-2 orders of magnitude on GPU while maintaining competitive compression ratios and rendering quality.
-
Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals
A continuous-token model with shared Haar wavelet coefficients reports 39.92 dB audio, 29.37 dB image, and 23.93 dB video PSNR on three datasets and shows energy-based selection outperforms uniform selection by roughly 16 dB.
-
MMGS: 10$\times$ Compressed 3DGS through Optimal Transport Aggregation based on Multi-view Ranking
MMGS compresses 3DGS models to 10% primitives with preserved rendering quality and 10x training speedup by combining multi-view geometric ranking with global OT aggregation and densification.
-
$\mathcal{P}^3$: Toward Versatile Embodied Agents
P^3 combines real-time perception, feedback-free tool use, and priority-based dynamic scheduling into a unified framework for embodied agents.
Discussion (0). Sign in to comment.