FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting

Yuxuan Li; Yihang Chen; Yufeng Zhang; Jianfei Cai; Weiyao Lin

cs.CVSep 27, 2026

FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting

Authors: Yuxuan Li, Yihang Chen, Yufeng Zhang, Jianfei Cai, Weiyao Lin

Organizations: Shanghai Jiao Tong University · Monash University

Abstract

Feed-forward 3D Gaussian Splatting (3DGS) enables efficient novel-view synthesis from sparse multi-view images, yet its representations remain costly to store and transmit. Existing approaches compress either the input images, incurring heavy receiver-side reconstruction, or the reconstructed Gaussian primitives, which are difficult to compress due to their heterogeneous and irregular attributes. We instead compress compact intermediate features, providing a better balance between compression efficiency and receiver-side complexity. Based on this paradigm, we propose FeCoSplat, a feedback-guided compression framework for feed-forward 3DGS. FeCoSplat first compresses multi-view features to obtain an intermediate 3DGS, whose rendered views are used as feedback to guide a second-stage compression for further refinement. The resulting bitstreams are decoded into a compact implicit state, from which the final Gaussian primitives are reconstructed with a lightweight predictor. Experiments demonstrate that FeCoSplat achieves favorable rate--distortion performance, particularly at low bitrates, while requiring only 3.45M parameters for receiver-side Gaussian reconstruction. Code will be released soon.

Figures & tables

Figure 1: Different compression pipelines for feed-forward 3DGS. Image compression incurs high receiver-side computation and error propagation, while Gaussian compression is challenged by heterogeneous attributes and irregular primitives. Feature compression operates on compact, structured intermediate feature representations, balancing efficiency and receiver-side complexity.

Figure 2: Overview of FeCoSplat . Top: FeCoSplat performs feature compression in two stages, encoding multi-view features in Stage 1 ( $k=1$ ) and feedback-guided features in Stage 2 ( $k=2$ ). A unified feature codec integrates the decoded information into an implicit Gaussian state across stages, followed by geometry-aware 3D interaction for Gaussian reconstruction. Bottom Left: Encoding process, where reconstruction feedback is introduced to capture complementary information, and $\bm{S}_{1}$ serves as decoded context to reduce redundancy in the second-stage coding. Bottom Right: Decoding process, which requires only lightweight modules and avoids explicitly reconstructing the intermediate 3DGS. The encoding and decoding of camera parameters are omitted for clarity.

Methods	Params (M) $\downarrow$		Latency (s) $\downarrow$		Peak GPU Mem (GiB) $\downarrow$
Methods	Sender	Receiver	Sender	Receiver	Peak GPU Mem (GiB) $\downarrow$
Image Compression
ELIC $\rightarrow$ DepthSplat	26.45	62.40	0.077	0.181	1.505
ELIC $\rightarrow$ ReSplat	26.45	100.69	0.074	0.305	2.119
Gaussian Compression
DepthSplat $\rightarrow$ TinySplat	38.34	N/A *	1.285	0.375	1.365

Table 1: Comparison of sender/receiver parameters, coding latency, and peak GPU memory on RealEstate10K. FeCoSplat achieves a lightweight receiver with only 3.45M parameters and 0.117s latency, while also maintaining low sender-side complexity and the lowest peak GPU memory among the compared learning-based approaches.

Figure 3: Performance comparison.

Figure 4: Qualitative comparison on RE10K and ACID. The rate per scene (KiB), PSNR (dB), and LPIPS are reported for each result. FeCoSplat achieves favorable reconstruction quality even at extremely low bitrates compared with competing methods. More results are in Appendix D .

Figure 5: Ablation study on component design, Gaussian resolution and feedback iterations.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A1: Detailed architectures of the building blocks and feature extractors used in FeCoSplat . The figure illustrates the designs of the 2D and 3D blocks, together with the multi-view feature extractor $\mathcal{E}_{\mathrm{mv}}$ and the feedback-guided feature extractor $\mathcal{E}_{\mathrm{fb}}$ .

Figure A2: Performance comparison with two base-size methods.

Methods	Sender Params (M) $\downarrow$	Receiver Params (M) $\downarrow$
ELIC $\rightarrow$ DepthSplat (Base)	26.45	144.81
ELIC $\rightarrow$ ReSplat (Base)	26.45	246.20
FeCoSplat	37.28	3.45

Appendix

Table A1: Comparison of model parameters at the sender and receiver.

Figure A3: Visualization of the two-stage reconstruction process of FeCoSplat .

Figure A4: Qualitative comparison on DL3DV.

Explore similar work

CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting
May 25, 2026Pengpeng Yu, Runqing Jiang, Qi Zhang +3Feed-Forward 3D Gaussian Splatting Feed-Forward 3D Gaussian
Sun Yat-sen University, China · Pengcheng Laboratory, China · Peking University, China
GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion
Jul 27, 2026Qiang Hu, Zhenlong Wu, Lei Huang +3Feed-Forward 3D Gaussian Splatting Feed-Forward 3D Gaussian
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, China
Towards Practical Compression of 3D Gaussian Splatting
Sep 24, 2026Pengpeng Yu, Yueru Chen, Fei Song +4Large-Scale 3D Editing Dataset Novel View Synthesis
Sun Yat-sen University, China · Pengcheng Laboratory, China · Academy of Broadcasting Science, National Radio and Television Administration, China +1

May 25, 2026, cs.CVJ/K move · Enter open · S save

CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting

Pengpeng Yu, Runqing Jiang, Qi Zhang, Dingquan Li+2

Sun Yat-sen University, China · Pengcheng Laboratory, China · Peking University, China

Feed-Forward 3D Gaussian Splatting Feed-Forward 3D Gaussian 3D Gaussian Primitives 3D Generation

Open paper AskPDF arXiv

While feed-forward 3D Gaussian splatting reconstructs renderable Gaussian primitives from sparse context views without per-scene optimization, existing pipelines do not provide a compact scene representation for storage or transmission. A natural solution is to apply existing 3DGS compression methods to the generated Gaussian primitives. However, this approach operates on the final irregular 3D representation and is decoupled from the internal feature-to-Gaussian generation process, which limits compression efficiency. To address this, we introduce CodecSplat, an ultra-compact latent coding framework for feed-forward 3D Gaussian splatting. CodecSplat first encodes an intermediate 2D Gaussian-generation feature into an entropy-coded scene bitstream. At the decoder, the latent feature is reconstructed and used to predict depth and Gaussian parameters, which are then mapped to 3D Gaussian primitives. Note that, by integrating compression into the feed-forward Gaussian generation pipeline, CodecSplat avoids inefficient compression over irregular 3D Gaussian primitives and allows the codec to exploit the structured intermediate feature representation. We instantiate CodecSplat on a feed-forward Gaussian splatting backbone with depth-guided multi-view feature refinement and a hierarchical learned feature codec. On DL3DV and RealEstate10K datasets, CodecSplat achieves 23.56-26.36 dB and 24.76-27.05 dB PSNR with only 20.00-107.77 KiB and 3.37-12.51 KiB per scene, respectively. This is roughly one order of magnitude smaller than compressing feed-forward generated Gaussian primitives, while preserving controllable rate-distortion behavior.

Abstract

Explore similar work

CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting

GenSplatCodec: Feed-Forward Gaussian Splatting Compression via One-Step Diffusion

Towards Practical Compression of 3D Gaussian Splatting