Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations

Youyu Chen; Junjun Jiang; Yueru Luo; Kui Jiang; Xianming Liu; Xu Yan; Dave Zhenyu Chen

cs.CVApr 1, 2026

Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations

Authors: Youyu Chen, Junjun Jiang, Yueru Luo, Kui Jiang, Xianming Liu, Xu Yan, Dave Zhenyu Chen

Organizations: Harbin Institute of Technology · The Chinese University of Hong Kong, Shenzhen · Huawei

Abstract

With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up. In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models. At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs.

Figures & tables

Figure 1: In this paper, we propose Reliev3R, the first learning paradigm to train a F eed- f orward R econstruction M odel (FFRM) from scratch without reliance on multi-view geometric annotations. To learn multi-view geometric knowledge, Reliev3R engages pseudo monocular relative depths and pseudo image sparse correspondences with multi-view geometry constraints as a weakly-supervised learning objective. As shown above in the figure, Reliev3R surpasses early FFRMs ( e.g . , FLARE [ 43 ] ) and weakly-supervised camera pose estimation models ( e.g . , AnyCam [ 33 ] ) in the overall performance.

Figure 2: Similar to prior FFRMs, Reliev3R performs geometry reconstruction in a single forward pass given a group of images. However, the supervision of Reliev3R doesn’t depend on any multl-view geometric annotations ( e.g . , ground truth of point maps and camera poses which are registered in 3D world coordinate). Specifically, instead of directly predicting the point maps registered in world coordinate, Reliev3R predicts view-wise depth maps, which are regularized with pseudo relative depth. To learn the registration of depth maps and camera poses in world coordinate, Reliev3R draws supervision signal from pseudo image correspondences. Pseudo annotations used by Reliev3R are produced with pretrained expert models (see Sec. 4.1 ). Experiments in Sec. 4 demonstrate that Reliev3R catches up with and even surpasses some of FFRMs supervised with multi-view geometric annotations.

Figure 3: Point map and camera pose visualization on DL3DV-benchmark dataset [ 16 ] . As shown above in the figure, Reliev3R delivers a visually comparable construction accuracy with the $\pi^{3}$ [ 32 ] while surpassing FLARE [ 43 ] in the overall performance. The camera pose of FLARE above is adopted directly from the model prediction instead of solved with PnP [ 6 ] based on point maps.

Method	Training with DL3DV-10K	MVG Annotations	Point Map		Camera Pose		Depth Map
Method	Training with DL3DV-10K	MVG Annotations	rel $\downarrow$	$\tau\uparrow$	ATE $\downarrow$	AUC $\uparrow$	rel $\downarrow$	$\tau\uparrow$
Map-Anything [ 13 ]	Yes	w	0.051	0.911	0.004	87.088	0.045	0.896
$\pi^{3}$ [ 32 ]	No	w	0.056	0.927	0.003	92.746	0.054	0.916
VGGT [ 28 ]	Yes	w	0.061	0.905	0.003	94.795	0.059	0.899
CUT3R [ 29 ]	Yes	w	0.073	0.815	0.005	89.432	0.072	0.801
$\pi^{3{\dagger}}$	Alone	w	0.057	0.897	0.013	63.314	0.047	0.890

Table 1: Evaluation on DL3DV-benchmark dataset [ 16 ] for 8-view reconstruction. Reliev3R outperforms early FFRMs trained with MVS annotations like MVDUSt3R [ 24 ] and FLARE [ 43 ] , demonstrating the advantage and potential of our method. We report the absolute relative error (rel) and inlier ratio at a relative threshold of 10% (

\tau

) to evaluate the reconstructed point maps and depth maps. We also report the average aligned trajectory error (ATE) and area under curve at an error threshold of 30

\degree{}

to evaluate camera pose estimation.

\pi^{3{\dagger}}

denotes the result of training

\pi^{3}

[ 32 ] from scratch on DL3DV-10K [ 16 ] dataset. We display if based methods are trained with/without multi-view geometric (MVG) annotations, and classify them by whether DL3DV-10K dataset is used for training into ‘Yes’, ‘Alone’ and ‘No’. SOTAs trained with 20 times of data for Reliev3R are colored as gray . Best , second best and third best are colored respectively.

Method	Point Map		Camera Pose		Depth Map
Method	rel $\downarrow$	$\tau\uparrow$	ATE $\downarrow$	AUC $\uparrow$	rel $\downarrow$	$\tau\uparrow$
$\pi^{3}$ [ 32 ]	0.027	0.972	0.002	86.941	0.027	0.970
$\pi^{3{\dagger}}$	0.232	0.678	0.028	17.789	0.220	0.171
AnyCam [ 33 ]	0.438	0.448	0.084	15.376	0.286	0.095
Reliev3R(ours)	0.172	0.594	0.030	15.711	0.124	0.583

Table 2: Zero-shot evaluation on ScanNet++ [ 39 ] dataset, which has a different focal length with DL3DV-10K dataset [ 16 ] that unseen during the training of Reliev3R and

\pi^{3{\dagger}}

. The same metrics are reported as in Tab. 1 . While consistently outperforming AnyCam [ 33 ] , Reliev3R matches against

\pi^{3{\dagger}}

in overall performance and holds significant advantage in depth prediction.

Figure 4: Visualization of point maps for zero-shot evaluation on ScanNet++ dataset [ 4 ] , which has a different focal length from DL3DV-10K dataest [ 16 ] . Reliev3R holds its advantage over AnyCam [ 33 ] even in zero-shot circumstance. Furthermore, Reliev3R presents a notable zero-shot performance on par with fully-supervised $\pi^{3{\dagger}}$ training on the same datasets.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Point Map		Camera Pose		Depth Map
Method	rel $\downarrow$	$\tau\uparrow$	ATE $\downarrow$	AUC $\uparrow$	rel $\downarrow$	$\tau\uparrow$
RayZer	$\times$	$\times$	0.786	0.362	$\times$	$\times$
Synth Fully-Sup.	0.277	0.475	0.042	17.075	0.208	0.357
+Reliev3R	0.137	0.667	0.033	34.240	0.106	0.679

Appendix

Table 3: We train

\pi^{3}

from-scratch (random initial weights) in a fully-supervised manner on a down-sampled mixture of synthetic datasets, including ASE dataset, TartanAir dataset, MVS-Synth dataset, Blended-MVS dataset and etc . following VGGT. The training runs for 20k iterations with a total batch size of 128. The result is denoted as ‘Synth Fully-Sup’. in the table. Then we subsequently finetune this checkpoint on DL3DV dataset for 20k steps using the weak supervision proposed by Reliev3R. The result is denoted as ‘+Reliev3R’ in the table. We report the same metrics as Table 1 in the paper, which are evaluated on DL3DV-benchmark dataset. We also evaluate camera pose estimation for RayZer on this benchmark with its recently open-sourced checkpoint.

Figure 5: Visualization of input (multi-view images), output (multi-view depth maps and confidence maps) and pseudo labels (multi-view correspondences, monocular depth maps) of Reliev3R.

Figure 6: Visualization of input (multi-view images), output (multi-view depth maps and confidence maps) and pseudo labels (multi-view correspondences, monocular depth maps) of Reliev3R.

Figure 7: Visualization of input (multi-view images), output (multi-view depth maps and confidence maps) and pseudo labels (multi-view correspondences, monocular depth maps) of Reliev3R.

Figure 8: Visualization of input (multi-view images), output (multi-view depth maps and confidence maps) and pseudo labels (multi-view correspondences, monocular depth maps) of Reliev3R.

	Point Map		Camera Pose		Depth Map
	rel $\downarrow$	$\tau\uparrow$	ATE $\downarrow$	AUC $\uparrow$	rel $\downarrow$	$\tau\uparrow$
$\alpha=0.2$	0.143	0.607	0.022	42.116	0.137	0.606
$\alpha=0.5$	0.137	0.626	0.023	46.703	0.129	0.628
$\alpha=1.0$ (Reliev3R)	0.122	0.663	0.018	49.426	0.115	0.657
$\alpha=2.0$	0.127	0.651	0.020	48.566	0.121	0.647

Appendix

Table 4: Ablation study on different confidence weight

\alpha

on DL3DV-benchmark [ 16 ] dataset.

\alpha=1.0

is adopted in the paper to train Reliev3R. Absolute relative error (rel) and inlier ratio at a relative threshold of 10% (

\tau

) are reported to evaluate point maps and depth maps. Average aligned trajectory error (ATE) and area under curve at an error threshold of 30° (AUC) are reported to evluate camera pose estimation.

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