CompMVR: Compression-Aware Multi-View Restoration Using Diffusion Models for Geometrically Consistent 3D Reconstruction

Dong-hwi Kim; Chaewon Moon; Hojun Song; Dongbeom Kim; Junyeong Jang; Aro Kim; Gahyeon Kim; Heejung Choi

cs.CVOct 23, 2026

CompMVR: Compression-Aware Multi-View Restoration Using Diffusion Models for Geometrically Consistent 3D Reconstruction

Authors: Dong-hwi Kim, Chaewon Moon, Hojun Song, Dongbeom Kim, Junyeong Jang, Aro Kim, Gahyeon Kim, Heejung Choi,

Organizations: Kyungpook National University, Republic of Korea · Adobe Research, USA

Abstract

Real-world 3D reconstruction pipelines often rely on multi-view images degraded by lossy compression. While compression artifacts have been extensively studied in single-image or video restoration, their impact on multi-view geometry remains largely overlooked. In this work, we identify compression-induced cross-view geometric inconsistency as a critical failure mode, where view-dependent artifacts disrupt feature matching, dense correspondence, camera parameter estimation, and downstream 3D reconstruction tasks such as novel view synthesis. To address this problem, we propose CompMVR, a compression-aware multi-view diffusion restoration framework that jointly refines compressed views while preserving cross-view consistency, and construct compressed datasets for systematic evaluation. Unlike conventional restoration methods that optimize each image independently for 2D perceptual quality, CompMVR leverages learned compression priors and cross-view correspondence to recover visual details while improving geometric reliability across views. Experiments across diverse datasets, codecs, and compression levels demonstrate gains in restoration quality and downstream 3D tasks, including novel-view synthesis, camera pose estimation, and view matching. These results establish compressed multi-view restoration as a distinct problem and highlight its importance for 3D reconstruction under lossy compression. Code and data for this paper are available at https://mchaewon.github.io/CompMVR-project-page/.

Figures & tables

Figure 1. Effect of HEVC compression on VGGT feature matching in the Free/Lab scene. Lossy compression degrades cross-view correspondence: as the quantization parameter (QP) increases from 27 to 42, the number of matched features decreases compared to the uncompressed input. While existing diffusion-based compression-aware restoration methods recover visual quality, they do not consistently restore geometry-sensitive correspondences. Our method produces more reliable feature matches under both compression levels, demonstrating improved cross-view consistency for downstream 3D reconstruction.

Method		MeanMatches $\uparrow$	AUC@5 $\uparrow$
Uncompressed	GT	2,001	24.55
Compressed	HEVC, QP42	1,655	22.59
Restored	CODiff ∗	1,625	23.36
	DiQP ∗	1,643	23.45
	SIR-Diff ∗	1,627	23.09
	Ours	1,659	24.34

Table 1. Effect of lossy compression on VGGT view matching on RealEstate10K. We assess cross-view consistency restoration using the number of matched correspondences and AUC@5 for pose accuracy. Our method achieves the best view matching performance, outperforming diffusion-based image, video, and multi-view restoration baselines. The Best and Second-best results are highlighted in red and orange, respectively.

Figure 2. Overview of our proposed CompMVR. (a) In Stage 1, we learn a compression-aware latent representation by training a compression prior embedder. The latent is optimized through image reconstruction and supervision of coding parameters (codec type and QP). (b) In Stage 2, we perform multi-view restoration using a diffusion model conditioned on the learned compression prior. The latent is injected via cross-attention and fused with the UNet input, while a compression artifact estimator (CAE) predicts spatially varying residual maps for degradation-aware restoration. To enforce cross-view geometric consistency, we further apply a cross-view correspondence loss during training.

Figure 3. Energy maps computed from uncompressed images and artifact maps compressed at identical QP settings, for low and high complexity scenes. Higher scene complexity yields larger compression residuals.

Figure 4. Failure cases of a video diffusion restoration model ( Dehaghi et al., 2025 ) on multi-view images. Examples from the Free dataset (hydrant and pillar scenes, left to right) show that the model fails to maintain cross-view consistency. Highlighted regions reveal inconsistent restoration and geometric misalignment caused by large viewpoint differences.

Codec	QP	PSNR $\uparrow$					SSIM $\uparrow$					LPIPS $\downarrow$
Codec	QP	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours
AVC	27	22.35	20.83	20.50	19.72	22.90	0.601	0.541	0.625	0.512	0.642	0.309	0.378	0.393	0.426	0.236
AVC	42	22.01	21.39	19.95	19.61	23.21	0.644	0.586	0.571	0.517	0.688	0.382	0.433	0.472	0.462	0.325
HEVC	27	23.45	22.11	20.54	20.07	24.08	0.638	0.580	0.607	0.516	0.670	0.300	0.382	0.425	0.447	0.242
HEVC	42	22.96	22.49	21.10	19.90	24.49	0.687	0.605	0.595	0.517	0.718	0.285	0.403	0.451	0.464	0.265
AV1	27	22.37	20.87	20.27	19.63	22.84	0.605	0.550	0.404	0.512	0.647	0.321	0.379	0.618	0.431	0.238

Table 2. Quantitative novel-view synthesis results using 3D Gaussian Splatting on the Free dataset under different codec and QP settings. We report PSNR, SSIM, and LPIPS, where higher PSNR/SSIM and lower LPIPS indicate better rendering quality. The Best and Second-best results are highlighted in red and orange, respectively.

Codec	QP	AUC@15 $\uparrow$					AUC@30 $\uparrow$
Codec	QP	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours
AVC	27	64.72	72.27	70.05	73.28	77.52	77.36	81.04	81.30	81.71	85.00
AVC	42	61.37	69.36	66.16	69.50	73.22	76.20	79.02	78.69	79.26	82.79
HEVC	27	64.12	72.91	70.11	73.35	79.32	76.84	81.38	81.33	81.26	87.52
HEVC	42	63.77	71.44	69.70	69.61	75.26	76.00	79.82	80.75	79.07	83.70
VP9	27	61.90	73.14	70.85	72.65	77.56	75.38	81.45	81.95	80.92	85.32

Table 3. VGGT camera pose estimation results on the RealEstate10K dataset under different codec and QP settings. AUC@15 and AUC@30 are reported, where higher values indicate better pose estimation accuracy. The Best and Second-best results are highlighted in red and orange, respectively.

Codec	QP	AUC@5 $\uparrow$
Codec	QP	Compressed	CODiff ∗	DiQP ∗	SIR-Diff ∗	Ours
AVC	27	23.90	26.68	27.06	20.89	27.42
AVC	42	20.51	21.52	20.27	20.40	21.34
HEVC	27	26.03	25.96	25.26	21.81	29.46
HEVC	42	22.59	23.36	23.45	23.09	24.34
VP9	27	24.58	25.03	25.86	22.13	25.18

Table 4. VGGT view matching results on the RealEstate10K dataset under different codec and QP settings. We report AUC@5, where higher values indicate better pose estimation accuracy. The Best and Second-best results are highlighted in red and orange, respectively.

	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIPIQA $\uparrow$	MEt3R $\downarrow$
CODiff ∗	23.74	0.662	0.277	3.40	69.18	0.681	0.135
DiQP ∗	27.23	0.803	0.225	4.06	64.99	0.393	0.136
SIR-Diff ∗	20.91	0.794	0.426	3.83	60.99	0.565	0.141
Ours	23.89	0.897	0.281	3.26	68.08	0.673	0.132

Table 5. Quantitative comparison on the Free dataset under a matched fine-tuning protocol. We report 2D restoration metrics and MEt3R for evaluating image quality and multi-view geometric consistency. The Best and Second-best results are highlighted in red and orange, respectively.

Method	PSNR $\uparrow$	NIQE $\downarrow$	CLIPIQA $\uparrow$
CODiff ∗	23.4970	3.3412	0.6917
Ours	23.6228	3.2610	0.6981

Table 6. Generalization to the unseen VVC/H.266 codec on the Free dataset. VVC is not included during training. We report PSNR, NIQE, and CLIPIQA, where higher PSNR/CLIPIQA and lower NIQE indicate better restoration quality. The Best and Second-best results are highlighted in red and orange, respectively.

Codec	QP	PSNR $\uparrow$				SSIM $\uparrow$				LPIPS $\downarrow$				NIQE $\downarrow$				MUSIQ $\uparrow$				CLIPIQA $\uparrow$
Codec	QP	CODiff	DiQP	SIR-Diff*	Ours	CODiff	DiQP	SIR-Diff*	Ours	CODiff	DiQP	SIR-Diff*	Ours	CODiff	DiQP	SIR-Diff*	Ours	CODiff	DiQP	SIR-Diff*	Ours	CODiff	DiQP	SIR-Diff*	Ours
AVC	27	23.29	22.38	20.92	23.45	0.896	0.847	0.799	0.906	0.356	0.374	0.390	0.224	5.06	3.37	3.59	3.10	65.60	50.65	62.60	69.84	0.647	0.395	0.577	0.704
AVC	42	23.29	21.65	20.73	23.03	0.869	0.802	0.778	0.857	0.458	0.484	0.461	0.391	5.37	4.47	4.07	3.25	58.48	40.66	61.20	65.479	0.529	0.315	0.552	0.610
HEVC	27	24.42	23.12	21.27	25.98	0.897	0.831	0.796	0.916	0.355	0.382	0.447	0.218	5.06	3.49	3.92	3.24	65.02	46.49	58.36	68.39	0.634	0.360	0.555	0.689
HEVC	42	24.50	22.70	21.20	25.61	0.889	0.811	0.789	0.900	0.409	0.451	0.477	0.320	5.27	4.06	4.21	3.42	62.54	41.93	58.18	67.11	0.581	0.330	0.547	0.640
AV1	27	23.30	22.34	20.91	23.74	0.897	0.845	0.798	0.906	0.359	0.382	0.397	0.236	5.05	3.47	3.62	3.20	65.82	50.48	62.89	69.51	0.645	0.401	0.577	0.709

Table 7. Quantitative comparison of 2D restoration methods on the Free dataset. We evaluate various 2D diffusion-based restoration models under different codec and QP settings. The Best and Second-best results are highlighted in red and orange, respectively.

Figure 5. t-SNE visualization of the Stage 1 latent representations learned by using $\mathcal{L}_{\text{codec}}$ and $\mathcal{L}_{\text{QP}}$ , and their combination, respectively. Color denotes codec type (AVC / HEVC / AV1 / VP9) and point size denotes QP level.

$\mathcal{L}_{\text{codec}}$	$\mathcal{L}_{\text{QP}}$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIPIQA $\uparrow$
✓		23.49	0.887	0.304	3.40	63.69	0.613
	✓	23.77	0.896	0.282	3.32	68.42	0.676
✓	✓	23.89	0.897	0.281	3.26	68.08	0.673

Table 8. Ablation study on Stage 1 supervision losses,

\mathcal{L}_{\text{codec}}

and

\mathcal{L}_{\text{QP}}

, evaluated by the final Stage 2 restoration performance. The Best and Second-best results are highlighted in red and orange, respectively.

Conditioning	Sampson $\downarrow$	MEt3R $\downarrow$
Null embedding	0.081	0.1361
Learned prior	0.078	0.1357

Table 9. Geometric ablation of the learned Stage-1 compression prior. The Best and Second-best results are highlighted in red and orange, respectively.

Conditioning	Inlier Ratio $\uparrow$	RANSAC Inliers $\uparrow$	Sampson $\downarrow$	AUC@5 $\uparrow$	MEt3R $\downarrow$
Random	0.7747	97.7	0.0960	0.5164	0.1494
Learned	0.8047	130.1	0.0806	0.5275	0.1404

Table 10. Robustness analysis of the learned compression prior against random conditioning. The Best and Second-best results are highlighted in red and orange, respectively.

Compression Prior	CAE	$\mathcal{L}_{\text{corr}}$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIPIQA $\uparrow$
✓			23.79	0.893	0.305	3.16	62.16	0.595
✓	✓		23.71	0.892	0.293	3.27	67.42	0.672
✓		✓	23.82	0.896	0.287	3.39	67.68	0.665
	✓	✓	23.87	0.898	0.267	3.54	67.11	0.669
✓	✓	✓	23.89	0.897	0.281	3.26	68.08	0.673

Table 11. Ablation study on Stage 2 components, including the compression prior extracted from Stage 1, the compression artifact estimator (CAE), and the cross-view correspondence loss

\mathcal{L}_{\text{corr}}

. The Best and Second-best results are highlighted in red and orange, respectively.

Method	Params (M) $\downarrow$	Time (s) $\downarrow$	Memory (GB) $\downarrow$
DiQP	79.373	0.422	1.02
CODiff	93.113	0.699	2.89
SIR-Diff	815.545	0.826	2.48
Ours	276.171	1.073	11.01

Table 13. Computational complexity of the compared restoration methods. We report the number of trainable parameters, forward inference time, and peak GPU memory usage.

Figure 6. Qualitative comparison on the Free dataset. The top rows correspond to an indoor scene (Lab), and the bottom rows correspond to an outdoor scene (Sky). The Lab scene is compressed using HEVC with QP 42, while the Sky scene is compressed using AV1 with QP 42. Highlighted regions reveal that our method better preserves fine details and structures under severe compression.

Figure 7. Visualization of estimated artifact maps on the Free dataset. The estimated artifact maps highlight compression-sensitive regions, such as edges and texture-rich structures, demonstrating that our CAE effectively captures spatially varying degradation cues.

Figure 8. Qualitative comparison of synthesized novel 3D views from restored multi-view inputs on the Free dataset. We evaluate global consistency using scene renderings synthesized from multiple viewpoints and local structural fidelity using zoomed-in regions. Views 1 and 2 correspond to HEVC QP42, and View 3 corresponds to AV1 QP42. Our method preserves sharper geometry-critical details and reduces blur and distortion compared with diffusion-based restoration baselines. Red boxes indicate representative regions for comparison.

Figure 9. Qualitative novel-view synthesis results rendered at ground-truth target camera poses. The top two rows correspond to HEVC QP42, and the bottom two rows correspond to AVC QP42. We compare each method with the corresponding GT view using full-scene renderings and zoomed-in regions. Our method better preserves object structures, text regions, and high-frequency details, producing results closer to GT than diffusion-based restoration baselines. Red boxes indicate representative regions for comparison.

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Abstract

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