NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction
Authors: Weirong Chen
Chuanxia Zheng
Ganlin Zhang
Andrea Vedaldi
Daniel Cremers
Venue: ICLR 2026 Poster (2026)
Keywords: Feed-forward 3D Reconstruction
Non-pixel-aligned 3D Reconstruction
3D Completion
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Given unposed multi-view images, NOVA3R recovers complete, non-overlapping 3D geometry, reconstructing visible and occluded regions with physical plausibility.

Abstract: We present NOVA3R, an effective approach for non-pixel-aligned 3D reconstruction from a set of unposed images in a feed-forward manner. Unlike pixel-aligned methods that tie geometry to per-ray predictions, our formulation learns a global, view-agnostic scene representation that decouples reconstruction from pixel alignment. This addresses two key limitations in pixel-aligned 3D: (1) it recovers both visible and invisible regions with a complete scene representation, and (2) it produces physically plausible geometry with fewer duplicated structures in overlapping regions. To achieve this, we introduce a scene-token mechanism that aggregates information across unposed images and a diffusion-based 3D decoder that reconstructs complete, non-pixel-aligned point clouds. Extensive experiments on both scene-level and object-level datasets demonstrate that NOVA3R outperforms state-of-the-art methods in terms of reconstruction accuracy and completeness.

Published as a conference paper at ICLR 2026
NOVA3R: NON-PIXEL-ALIGNED VISUAL TRANS- FORMER FOR AMODAL 3D RECONSTRUCTION
Weirong Chen Chuanxia Zheng Ganlin Zhang Andrea Vedaldi Daniel Cremers
TTehhiica University of Munich Munich Center for Machine Learning 3 University of Oxford
*Nanyang Technological University
ABSTRACT
We present NOVA3R, effective approach for non-pixel-aligned 3D reconstruc- tion from set of unposed images a freeard manner. Unlike pixel-aligned methods that tie geometry to per-ray predictions, view-agnostic scene representation that decouples reconstruction from pixel align- ment. This addresses key limitations in pixel-aligned 3D: (1) it recovers both visible and invisible points with complete scene representation, and (2) pro- duces physically plausible geometry with fewer duplicated structures in overlap- ping regions. To achieve this, we introduce scene-token mechanism that aggre- gates information across unposed images and diffusion-based 3D decoder that reconstructs complete, non non--ixel-au pixel-aligned point clouds. Extensive experiments on both scene-level and object-level datasets demonstrate that NOVA3R outper- forms state-of-the-art methods in terms of reconstruction accuracy and complete- ness. Our project page available at: https: htt.///wchn/33..m g thub. o nova3r.
formulation learns global,
Color by Dopth
Color by Density
Pixel-
aligned
VGGT
Incorsplete
High density Duplicated
Input
Per-frame Ropresentation
Visible Only
Initial
Non-pixel-
aligned
NOVA3R
Input
Global Representation
Complete 3D
Physically Plausible
Figure I: NOVA3R enables non non-pixel-- pixel-aligned reconstruction by learning a global scene representa- tion from unposed images. Compared to pixel-aligned methods, NOVA3R recovers both visible and occluded regions and produces more physically plausible geometry with fewer duplicated structures.
INTRODUCTION
We consider the problem of non-pixel-aliinedd 3D reconstruction from more unposed images, in feed-forward manner. This is challenging task, as the model must infer global, view-agnostic representation of the scene without relying on per-ray supervision. This formulation avoids the lim- itations of pixel-aligned methods, which reconstruct only visible surfaces and often produce redun- dant geometry in overlapping regions. It therefore enables more complete and physically plausible 3D reconstruction, capturing both visible and occluded structures in consistent manner.
Recent work in 3D reconstruction has largely focused on the pixel-aligned formulation, where ge- ometry is predicted in the form of depth maps, point maps, or radiance fields tied to the image plane.
Project Lead

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Published as a conference paper at ICLR 2026
Dec.
Diffusion Trans former
FM Dec.
Transformer
frameformer former
Noisy Latent
Scene
Token
VGGT #Params 941
TRELLIS 4Params.
1785 M
Ours HiParams. 718 M
Pixel-aligned Reconstruction
Latent 3D Generation
Non-pixet-aligned Reconstruction
Figure 2: Comparison of different reconstruction paradigms. Our non-pixel-aligned approach combines feed-forward efficiency with global, view-agnostic scene representation, removing the reliance on pixel-level supervision. NOVA3R provides unified solution for various reconstruction tasks, achieving multi-view consistency and geometrically faithful results.
DUS13R (Wang et al., 2024a) pioneers this paradigm of dense, pixel-aligned 3D reconstruction from unposed image collections, achieving impressive results in reconstructing the visible regions of scene. Building on this, follow-up works (Tang et al., 2025b; Wang et al., 2025b; Yang et al., 2025; Zhang al., 2025b; Wang et al., 2025a) extend DUS(3R from image pairs to multi-view settings, en- abling feed-forward 3D geometry reconstruction from larger image sets. However, the pixel-aligned formulation remains tied to per-ray prediction, which restricts reconstruction to visible regions and yields incomplete geometry and overlapping point layers in areas visible to multiple cameras.
Another line of work explores latent 3D generation, which learns a global representation in a com- pact latent space and decodes it into voxels or meshes (Vahdat et al., 2022; Zhang et al., 2023; 2024b; Ren et al., 2024; Xiang et al., 2025a; Tochilkin et al., 2024; Team, 2024; 2025; et al., 2025b). While this global formulation can plausibly complete occluded regions beyond the input views, most approaches remain confined to the object level. They assume canonical space and re- quire high-quality mesh supervision, which makes these methods struggle with complex, cluttered scenes. For scene-level reconstruction, some methods (Chen et al., 2024; Liu et al., 2024; Gao al., 2024: Szymanowicz al., 2025) inpaint unseen regions by synthesizing novel views with pre-trained diffusion models and then post-process to recover geometry. However, such pipelines do not guarantee physically meaningful point clouds.
To overcome these limitations, we introduce the Non-pixel-aligned Visual Transformer (NOVA3R) (see Figure 1). First, we address the challenge of non -ixeele---ll supervision by leveraging ieffusioonbbsed 3D autoencoder. It first compresses complete point clouds into compact latent to- kens, and then decodes them back into the original space, supervised with a flow-matching loss that resolves matching ambiguities in unordered point sets. Recent works on 3D autoencoders (Zhang et al., 2023; Xiang et al., 2025a; Team, 2024; et al., 2025b) have demonstrated the effectiveness of latent representations, but they primarily designed for object reconstruction, assuming high- quality meshes for supervision. In contrast, our formulation targets scene-level reconstruction and requires only point clouds derived from meshes depth maps for supervision, enabling it to capture priors of complete 3D scenes and produce physically coherent geometry without duplicated points.
Second, tackle the problem of mapping unposed images a global scene representation. Train- ing such a model directly would require massive amounts of complete scene data and computa- tional resources. To improve generalization, our model is built on a pre-trained image encoder from VGGT (Wang et al., 2025a), augmenting it with learnable scene tokens that aggregate information from arbitrary numbers of views and map them into the latent space of our point decoder. This design enables NOVA3R to support both monocular and multi-view reconstruction, without being restricted fixed number of inputs. Despite being trained on relatively small datasets, our model generalizes well to unseen scenes, achieving complete and physically plausible reconstructions.
In summary, our main contributions are as follows: (i) We introduce unified non-pixel-aligne reconstruction pipeline with minimal assumptions, applicable to both object-level and scene-level complete reconstruction tasks. (ii) We address key limitations of pixel-aligned methods, which of- ten produce incomplete point clouds, duplicated geometry, and 3D inconsistencies in overlapping regions. By contrast, our pon-pixl--aligned formulation naturally yields complete and evenly dis- tributed geometry. (iii) We integrate feed-forward transformer architecture with a lightweight flow- matching decoder, effectively bridging the gap between pixel-aligner reconstruction and latent 3D generation, combining feed-forward efficiency with strong 3D modeling capability (see Figure 2).
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Stage 3D Latent AutoEncoder
kyy
Path Sempler
Croes-art
Self-attn
Initial Query
FM Decoder
Par-viow Point Cloud
Point Encoder
Stage 2: Food-forward Scene Predication
DINO
Scene
Loodl
Global-ami -ift
Local
FM Decoder
xI2
Initial Secoe Token
Image Encoder
Point Decoder
Point Clouds
Figure 3: Overview of NOVA3R. Stage 1: a 3D point autoencoder encodes complete point clouds into latent scene tokens and decodes them with flow-matching (FM) decoder. Stage 2: an image encoder with learnable scene tokens integrates multi-view information into a unified scene latent space, supervised by the FM loss with the Stage-1 decoder frozen. During inference, only the second stage pipeline is used to produce complete, non con-pixl--aligeed point cloud.
2 RELATED WORK
Feed-Forward 3D Reconstruction. Unlike per-scene optimization methods (Mildenhall et al., 2020; Kerbl et al., 2023) that iteratively refine a 3D representation for each individual scene, scene,feed- forward 3D reconstruction approaches aim to generalize across scenes by predicting 3D geometry directly from set input images in a single pass of a neural network. Early approaches typi- cally focus predicting geometric representations, such depth maps (Eigen & Fergus, 2015), meshes (Wang et al., 2018), point clouds (Fan et al., 2017), or voxel grids (Choy et al., 2016), and trained on relatively small-scale datasets (Nathan Silberman & Fergus, 2012; Chang al., 2015). As result, these models struggled to capture fine-grained visual appearance and exhibited limited generalization unseen scenes.
More recently, DUSI3R (Wang et al., 2024a) and MASt3R (Leroy et al., 2024) directly regress dense, pixel-aligned point maps from unposed image collections. These approaches mark sig- nificant step toward generalizable, pose-free 3D reconstruction. Building on this paradigm, many recent works (Tang al., 2025b; Wang al., 2025b; Yang et al., 2025; Zhang et al., 2025b; Wang et al., 2025a) extend it from image pairs to multi-view settings, enabling feed-forward 3D geometry reconstruction from sets of uncalibrated images. However, these pixel-aligned methods produce in- complete geometry and duplicated points in overlapping regions. In contrast, approach outputs a unified and complete 3D reconstruction that integrates both visible and occluded regions.
Complete 3D Reconstruction. To achieve complete 3D reconstruction, existing approaches typ- ically follow two main paradigms. One line work (Vahdat et al., 2022; Zhang et al., 2023; Zhao al., 2023; Zhang et al., 2024b; Ren al., 2024; Xiang et al., 2025a; Tochilkin al., 2024; Team, 2024; 2025; Li et al., 2025b) leverages compact latent spaces (Rombach et al., 2022) or large-scale networks (Hong al., 2024: Zhang al., 2024a; Tang et al., 2025a) for generating complete 3D assets. While effective, these approaches primarily target individual object reconstruction and fall short in modeling complex. cluttered scenes. The other paradigm fine-tunes large-scale pre-trained diffusion models (Rombach et al., 2022; Blattmann et al., 2023). For objects. a notable example Zero-1-ae --to3 (Liu et al., 2023b), which conditions on camera pose for high-quality 360-degree novel view rendering by training lowed by a large group of successors (Long et al., 2024; Shi et al., 2024; Han et al., 2024: Liu al., 2023a; Li al., 2024; Zheng & Vedaldi, 2024; Ye et al., 2024; Voleti et al., 2024). For scenes, several recent approaches aim to achieve complete 3D geometry by leveraging controlled camera trajectories (Wang al., 2024b; Sargent et al., 2024; Wu et al., 2024; Gao al., 2024; Walling- ford et al., 2024; Zhou et al., 2025) or introducing auxiliary conditioning signals (Liu et al., 2024; Yu al., 2025b; Chen et al., 2024; Yu al., 2025a). However, these methods do not explicitly
a huge dataset, Objaverse (Deitke et al., 2023). This is fol-
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reconstruct the complete underlying 3D geometry. More recently, WVD (Zhang et al., 2025a) and Bolt3D (Szymanowicz et al., 2025) propose a hybrid RGB+point map representation to combine geometry and uppearance for 3D reconstruction; however, they still require known camera poses for novel RGB+poinn map rendering. We address pose-free 3D reconstruction from unconstrained im- ages, and provide complete 3D representation. More closely related our work, Amodal3R (Wu et al., 2025) introduces amodal 3D reconstruction to reconstruct complete 3D assets from partially visible pixels, but it still works only on objects.
3 METHOD
Given set of unposed images I { a neural network that directly produces a complete 3D point cloud, both in terms of visible and occluded regions. We first discuss the problem formulation in Section 3.1, followed by our 3D latent autoencoder in Section 3.2, and we finally describe our global scene representation in Section 3.3.
(I' RHxxx3) of scene, our goal is learn
3.1 PROBLEM FORMULATION
Problem Definition. The input to our model is a set of K unposed images I = {r of a scene, and the output a complete 3D point cloud P E Nx3 using a ferd-forward neural network & : + P. This task is conceptually similar to the conventional feed-forward 3D reconstruction setting (Wang et al., 2024a; 2025a; Jiang et al., 2025), except that here N represents the number of points in the complete scene point cloud (as shown in Figure 4), rather than K x H x W points paokeproeected from all pixels in each input image.
The key observation is that scene in the real world is composed of fixed set of physical points, regardless of how many images are captured from different viewpoints. a physical 3D point is observed in multiple 2D images, the correct representation of the scene should contain single point, rather than duplicated points back-projected from each observation. Conversely, even if a physical 3D point is never observed in any image, it still exists in the real world and should be inferred by the model. Therefore, the model should be able to predict the occluded regions of the scene and avoid generating redundant points in the overlapping visible regions.
Data Preprocessing, The key to training such a model is the definition of the complete 3D point clouds of scene. It must contain points in both visible and occluded regions, and avoid duplicated points in the overlapping vis- ible regions. The visibility of a 3D point is de- fined with respect to the input images I. How- ever, the notion of invisible points is ambigu- ous: there
Observation
Visible
Complete
Figure 4: Visible point clouds vs. complete point clouds. Our NOVA3R aims to recover the complete geometry within the input view's 's frustum.
infinitely many points that not visible in input images, or even outside the field of view of all input images. To simplify the problem, as shown in Figure 4, we define invisible points within the input-view frustum and discard points outside the frustum.
Creating such complete point clouds for supervision is non-trivial. The ideal solution is to use the ground-truth 3D mesh of the scene, which can be easily converted complete point cloud by uniformly sampling points on the mesh surface. However, the ground-truie -ruth mesh is not always available in scene-level datasets. When ground 3D meshes are not available, we instead ap- proximate the complete point clouds using depth maps aggregated from dense views. Specifically, we first back-project the depth maps from all dense views into point clouds, then apply voxel-grid filtering to remove duplicate points in overlapping visible regions. Finally, we discard points outside the frustum of the selected input views (single, two, or a set of views). During training, we apply the farthest point sampling method with random initialization to obtain a subset from the complete point cloud to train our point decoder.
Importantly, in DUSt3R (Wang et al., 2024a), complete point clouds are also view-agnostic: the 3D points are defined in the coordinate system of the first input view I, but are not pixel-aligned to any input images. This design allows the model to learn reconstruct the complete 3D scene in
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Sorne Taken
kvv
Self atm
Crocs aitn
ML
Cross-atto
MI.
MLF
MLF
Noisy Point
Lisc
Decoder (Independenl)
Decoder (Juint)
Result (Independent) Resulf (Joint)
Figure 5: Different Decoder Architectures. The independent decoder uses cross- aoosstat only, while the joint decoder implements an efficient self-attention, which yields more precise structures.
the first view's coordinate system while ignoring the ambiguity of pose estimation. Consequently, our model can be trained on a wide range of datasets without requiring ground-truth meshes, unlike existing object-level methods (Zhang et al., 2023; et al., 2025b; Team, 2024).
3.2 3D LATENT ENCODER-DECODEE WITH FLOW MATCHING
Following recent works in latent vector representation (Zhang et al., 2023), we design 3D latent transformer (Vaswani et al., 2017). However, ours does not require a perfect mesh as input or supervision. As shown in Figure 3 (Stage 1), we implement the model as a diffusion model.
The encoder Lene takes the point cloud P E gg-x3 input, and outputs a set of M latent tokens Z E RMxC,, In practice, to reduce the computational cost, the initial query points g E IR Ad XS are sampled from the complete point cloud P E R using farthest point sampling, where M < N. We further design a hybrid query representation by concatenating the point query with learnable tokens of the same length M along the channel dimension, followed by a linear projection layer that reduces the channel dimension from 20 to C.
Diflusion-based 3D AutoEncoder.
Once the latent tokens Z obtained, existing 3D VAE methods (Zhang al., 2023; Team, 2025; Li et al., 2025b) typically use a deterministic decoder to predict an occupancy field PPec [Z, x) or SDF values for our task, since obtaining ground-truth occupancy or SDF values for real scene-level datasets is costly or even infeasible. Importantly, unlike objects that can be enclosed within a canonical space, scenes typically lack well-defined boundaries and expand as the number of observations increases, making it difficult predefine a canonical space. Instead, we directly predict the 3D coordinates of each query point. However, because point clouds are not ordered or aligned, we cannot directly map the 3D point query to the ground-truth point clouds P using an L.2 loss. We then adopt a diffusion- based decoder dee (Z,, Z, t) to decode the scene tokens Z back to the original point cloud space. decoder takes as input a set N noised query point clouds Ie E N x3 at the flow matching time t, and the latent tokens Z as conditioning. The whole architecture is trained end-to-end with flow matching loss (Lipman et al., 2023):
deec (Z,x) for each 3D grid query x RNx3 However, this is not suitable
The transformer-basse
AE
Ee,
Paee Z,t) - (E- - x
where v, = (1 1)=00 + le. Note that we do not use KL. loss or any other regularization on the latent tokens as in existing 3D latent VAE methods (Team, 2025; Li al., 2025b).
Architecture. As noted above, our 3D autoencoder is implemented with a transformer architec- ture. Specifically, the encoder is built upon TripoSG (Li al., 2025b), which consists of one cross- attention layer and eight self-attention layers. The decoder has three transformer blocks (details are shown in Figure 5). Notably, the query will be switched between the 3D latent tokens Z and the noisy point clouds in each
layer. This design reduces the size of the self-attention maps while preserving information flow between latent tokens and query points. Concurrent work (Chang et al., 2024) also proposes eeffusiio-bbssd 3D latent autoencoder, but they consider 3D shape as a probability density function, and process each point independently.
3.3 SCENE REPRESENTATION WITH LEARNABLE TOKENS
We now describe how to learn a global scene representation from set unposed images. As shown in Figure 3 (Stage 2), implement it using a large transformer that takes the input images I and set of M learnable tokens l. MC input, and outputs the scene representation 2 E MxC.
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Table 1: Quantitative results for scene completion on SCRREAM (Jung et al., 2024). The one- side Chamfer Distance (GT - Prediction) results are shown in (). K is the number of input views. denotes methods that are not trained on scene-level data. Our method shows better completion results compared to other competitive baselines. Note that, since NOVA3R is a non-pixel-aligned
3D reconstruction model, it does not explicitly distinguish the visible and occluded points.
Visible (K=1)
Complete (K=1)
Complete (K=2)
Type
Method
CDJ
PS@0.11
FS@0.05t
CDL
FS@0.11 FS@0.051
CDL FS@0.11 PS@00051
Object
TRELLIS
TripoSG*
(0.301)
(0.268)
(0.420)
(0.418)
(0.301)
(0.313)
0.256
0.242
0.429
0.467
0.312
0.333
0.286
0.402
0.288
Metric3D-v2
0.063
0.803
0.534
0.086
0.725
0.473
Single- view
DepthPro
0.055
0.852
0.603
0.079
0.764
0.535
MoGe
0.035
0.945
0.786
0.063
0.836
0.668
LaRI
0.057
0.847
0,589
0,059
0.825
0,590
DUST3R
0.059
0.851
0.653
0.086
0.757
0,565
0.061
0.833
0,641
Multi- view
CUT3R
0.069
0.835
0.679
0.091
0.753
0.543
0.092
0.739
0.532
VGGT
0.041
0.923
0.754
0.070
0.810
0.657
0.065
0.821
0.606
Ours
(0.043)
(0.904)
(0.730)
0.048
0.882
0.687
0.053
0.862
0.657
Learnable Scene Tokens. As mentioned in Section 3.1, our model aims to predict a fixed number of non-pixel-aligned points under the first view's coordinate system. Accordingly, in addition to L, patchified image tokens t E RRL we introduce a set of M learnable global scene tokens ts E RRMxC which are randomly initialized and optimized during training. The combined token set E U from all input images, i.e., t U {+) }. and the learnable scene tokens ig. is fed into a large transformer, with multiple frame- and global-level self-attention layers. To simplify the architecture, the learnable scene tokens is are treated as global frame underlying the first view's s coordinate system. This means that the scene tokens undergo the same operations as the image tokens in each Transformer block, except that they use the first view's camera token.
Architecture. Our image encoder is built upon VGGT (Wang et al., 2025a), a representative feed- forward 31D reconstruction model. However, do not use its dense prediction heads predict the pixel-aligned depth and point maps. Instead, we use the output scene tokens 2 E RMxC as the conditioning of our point decoder Paec. to predict the con-pixel-aligned complete 3D point clouds The entire architecture is trained end-to-end with the flow matching loss:
P E R N
[| (12, 2,t) (c (c-
z00 20.1?]
C
flow
where dos is frozen in Stage and only the transformer D ty U - 2 and the learnable scene tokens is are optimized.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Metrics. Following Li et al. (2025a), we report Chamfer Distance (CD) and F-score (FS) at differ- ent thresholds (e.g.. O.1, 0.05) for completion tasks. For multi-view reconstruction tasks, we report accuracy (Acc), completion (Comp), and normal consistency (NC) following Wang et al. (2025b). Best results are highlighted as first, second and third..
Implementation Details. By default, we set the number of scene tokens as M = 768 and the number of points as N 10, 000 for training. The image encoder architecture is exactly the same as VGGT (Wang et al., 2025a), while the 3D latent autoencoder contains 8 layers in the encoder and 3 layers in the decoder. The training contains two stages. In Stage 1, we train the autoeneoder for 50 epochs. In Stage 2, we initialize the image encoder with VGGT pretrained weights and the flow-matching decoder with Stage-1 weights, then train for another 50 epochs. Note that, we only fine-tune the image encoder and the scene-token transformer in Stage 2. We train both stages by optimizing the dow-matching loss with the AdamW optimizer and a learning rate of 3e-4. The training runs on 4 NVIDIA A40 GPUs with a total batch size of 32. We use standard flow matching with cosine noise scheduling, timestep sampling in [0,1], a fixed 0.04 step size at inference, and identical loss settings for both object-level and scene-level datasets.
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Single-view Only
Input (n-1,2)
VGGT
LaRI
Ours
GT
Figure Qualitative results for scene completion on SCRREAM (Jung et al., 2024). Our method produces more complete point clouds with clearer and less distorted geometry than other baselines.
Table 2:
Quantitative results for hole area ratio and point cloud density variance on SCR- REAM (Jung et al., 2024). Our method significantly outperforms pixel-aligned baselines, achiev- ing both lower hole ratios and lower density variance.
Complete (K=l)
Complete (K=2)
Complete (K=4)
Method
Hole Ratiol
Density Var.
Hole Ratio
Density Var. +
Hole Ratio
Density Var. +
DUST3R
0.317
7.758
0.237
6.553
0.257
4.801
CUT3R
0.363
8.402
0.237
6.554
0.326
4.658
VGGT
0.307
7.105
0,238
6.546
0.261
5.217
Ours
0.088
5.127
0.121
2.188
0.134
1,881
4.2 SCENE-LEVEL RECONSTRUCTION
Datasets.
The scene-level model was trained on 3D-FRONT (Fu al., 2021) and Scan- Net++V2 (Yeshwanth et al., 2023), using the training splits from LaRI (Li et al., 2025a) and DUSt3 (Wang al., 2024a), which contain 100k and 230k unique images, respectively. For visible part training, we further incorporate ARKitScenes (Baruch et al., 2021). Ideally, our model is able to handle an arbitrary number input views, similar to VGGT (Wang et al., 2025a). However, limited by the available computational resources, mainly verify contributions two-view pairs and train with 1-2 input views.
To evaluate the cross-domain generalization ability of our model, directly evaluate performance on the unseen SCRREAM dataset (Jung et al., 2024), which provides complete ground-truth scans. We follow LaRI's setting for single-view evaluation, with 460 images for testing. For the two- view setting, we sample 329 pairs from the same scene with a frame-ID distance of 40-80, where the maximum pose gap is 30% (measured by point cloud co-visibility) and the hole area ratios (measured by completeness with threshold 0.1) range from 5.3% to 48.6%. We additionally eval- uate visible-surface multi-view reconstruction on the 7-Scenes (Shotton et al., 2013) and NRGBD datasets (Azinović et al., 2022), sampling input images at intervals of 100 frames.
Baselines. We compare NOVA3R with several representative scene-level 3D reconstruction meth- ods, including i) single-view Metric3D-v2 (Hu et al., 2024), DepthPro (Bochkovskiy et al., 2025), and MoGe (Wang al., 2025c); ii) multi-view DUSt3R (Wang al., 2024a), CUT3R (Wang al., 2025b), and VGGT (Wang et al., 2025a). However, these methods only focus pixel-aligned visi- ble 3D reconstruction. Hence, we further compare with the concurrent complete 3D reconstruction work LaRI (Li ct al., 2025a). Since it does not support multi-view inputs, for completeness, also report the results from object-level methods TripoSG (Li et al., 2025b) and TRELLIS (Xiang et al., 2025b) by disabling the input mask, while they not trained scene-level data.
Scene Completion. Following LaRI, we evaluate our amodal 3D reconstruction results on both visible and complete (visible occluded) regions. For visible setting, follow the same evalua- tion protocol as DUST3R (Wang et al., 2024a) and VGGT (Wang et al., 2025a), where the ground truth contains only the visible points from the input views. For the complete setting, the

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lligh
Low
Input (K=4)
CUT3R
VGGT
Ours
GT
Figure 7: Qualitative results for density evaluation NRGBD (K 4) (Azinović et al., 2022).
Yellow regions denote higher density, and purple regions denote lower density. Despite being trained with only two views, NOVA3R generalizes well to multiple views (K 4).
Table Quantitative results on visible reconstruction on 7-Scenes (K= (Shotton al., 2013).
Our NOVA3R model can be trained on RGB-D data and achieves competitive results compared to mie reconstruction methods. Note that, we use fewer tokens to represent a 3D scene.
Method
Tokens
Acc
Comp +
NC 1
Mcan
Med.
Mcan
Mcd.
Mcan
Med.
DUS:3R
2048
0.054
0.023
0.075
0.034
0.772
0.901
Spann3R
784
0.044
0.022
0.046
0.025
0.792
0,922
CUT3R
768
0.043
0.023
0.054
0.028
0.760
0.884
VGGT
2738
0.042
0.020
0.045
0.025
0.813
0,923
Ours
768
0.041
0.021
0.033
0.019
0.794
0.917
full point cloud as ground truth, including occluded and unseen regions. However, unlike pixel vay-conditional LaRI, NOVA3R does not explicitly identify the visible region. We therefore adopt one-sided Chamfer Distance (GT - Prediction) for the visible part: each GT-visible point must be explained by nearby prediction. This measures coverage of the visible ground truth, yet without penalizing missing, occluded regions. Table shows three settings: 1-view visible, 1-view complete, and 2-view complete. Despite using only two datasets train, method consistently outperforms multi-view baselines on complete reconstruction in both K = 1 and K = 2 settings, demonstrating the effectiveness of our pin--iiel-lliinee approach. Our method also achieves competitive results on visible-surfas reconstruction. Qualitative results in Figure 6 show that our method produces sur- faces without holes (unlike pixel-aligned methods such as VGGT) and yields clearer, less distorted geometry than LaRI. This benefit is attributed our non-pixel-aligned design, which prevents ray- direction bias in reconstruction. We further quantify the completion capability using the hole area ratio, which is computed by checking whether each ground-truth point has predicted point within distance threshold of 0.1. As shown in Table 2, our method consistently achieves significantly lower hole ratios, demonstrating its strong capability for complete reconstruction. In terms of density variance, our approach outperforms all pixel-aligned baselines, even in unseen four-view settings, indicating better physical plausibility with more evenly distributed point clouds. Moreover, when comparing across different K, the density variance consistently decreases from one to four input views, further confirming that incorporating more views leads to improved spatial uniformity.
Physically-plausible Reconstruction. Beyond 3D completion, our non-pixel-aligned formula- tion also features physically plausible reconstruction by fusing evidence in 3D rather than along camera-pixel rays, reducing duplicated points in overlapping regions and improving cross-view con- sisteney. To illustrate this, we evaluate visible reconstruction with K views on NRGBD (Azi- nović et al., 2022). As shown in Figure 7, pixel-aligned methods like CUT3R (Wang et al., 2025b) and VGGT (Wang et al., 2025a) accumulate 3D points in co-visible regions, producing uneven den- sities and multi-layer artifacts. This is physically incorrect, as each point corresponds to single location in the real world, regardless the number of views. In contrast, our NOVA3R generates cleaner, single-surface geometry with uniform point distribution, achieving competitive results de- spite using fewer datasets and views (see Table 3). We further quantify physical plausibility by computing the density variance in Table 2, which indicates that method achieves more uni- formly distributed reconstruction compared to pixel-aligned baselines.
4.3 OBJECT-LEVEL RECONSTRUCTION
Datasets. We demonstrate the versatility of our method unified non pixe--xel-lligned approach for both scenes and objects. Following Li et al. (2025a), we train an object-completion model on Objaverse (Deitke al., 2023) with 190k annotated images. For evaluation, we report results
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Table Quantitative results for object completion on GSO (Downs al., 2022). NOVA3R pro- vides unified solution for both scene and object completion from unposed images.
View-aligned (K=1)
View-aligned (K=2)
Type
Method
CD
FS@0.11 f
FS@0.05 f
CD FS@0.1 f
FS@0.05 1
SF3D
0.037
0.913
0.738
Single- view
SPAR3D
0.038
0.912
0.745
LaRI
0.025
0.966
0.894
TripoSG
0.025
0.961
0.899
Multi- view
TRELLIS
0.025
0,962
0.896
0.028
0,946
0.874
Ours
0.020
0.985
0.925
0.023
0.978
0.903
Input.(K (K-1) Lari
TRELLIS
Ours
GT
(K-2t
TRELLIS
Ours
GT
Figure 8: Qualitative results for object completion GSO (Downs al., 2022). Our method provides more precise geometry and better 3D consistency with multi-view inputs.
unseen Google Scanned Objects (Downs et al., 2022). For single- view reconstruction, we use the same 1030-object split LaRI (Li et al., 2025a). For two-view reconstruction, we fix the Oth image and uniformly sample three additional views, yielding three pairs per object (3090 pairs in total).
Baselines. We compare with several representative object-level 3D reconstruction methods, in- cluding SF3D (Boss et al., 2025), SPAR3D (Huang et al., 2025), TripoSG (Li et al., 2025b), and TRELLIS (Xiang et al., 2025b). We also include LaRI (Li al., 2025a) as a strong baseline, which is trained on the same dataset and supports amodal 3D reconstruction.
Object Completion. Table 4 reports results for single view (K 1) and views (K 2). Our NOVA3R outperforms LaRI on all three metrics. Importantly, our pipeline supports multi-view completion that maps different unposed images into the same view-aligned space. On the multi-view benchmark, our method also outperforms TRELLIS, highlighting the benefits of non pixel-aligned reconstruction for consistent global geometry. Qualitative comparisons in Figure show that our completions preserve fine structures, and achieve better 3D consistency in the multi-view setting.
4.4 ABLATION STUDIES
We perform comprehensive ablation studies the SCRREAM complete (K 1) setting validate the key design choices of our method, with particular emphasis on assessing the contribution of Scene Tokens global structure modeling. The results are summarized in Table 5, and discuss each component in detail below.
Initial Query (Stage 1). Prior work (Zhang al., 2023) shows that the initialization of point queries affects autoencoder performance. We compare three options: (i) downsampled input points, (ii) learnable query tokens, and (iii) hybrid that concatenates (i) and (ii). Downsampled points pre- serve the input geometry distribution, whereas Icarnable tokens add flexibility under source-target shifts. As shown in Table 5, the hybrid combines these benefits and yields the best results.
Number of latent scene tokens (Stage 1). As described in Section 3, we represent each scene latent tokens. The number of tokens M directly affects the representation capacity and ability to capture fine details, especially in large scenes. We evaluate different numbers of scene tokens from (256, 512, 768} and observe consistent improvements as the count increases (see Table 5). To balance accuracy and efficiency, could be further increased for better performance. We leave this for other works to explore.
with fixed-length
use M 768 tokens by default. Idcally, M
Different architecture of mlow-natching decoder (Stage 1). The latest work (Chang et al., 2024) also presents a flow now-matching decoder for point cloud encoder, but it assumes that all points are independent (shown in Figure 5). This design is efficient, but ignores spatial correlations between points. our work, we instead adopt a lightweight self-attention + moss-attention decoder that jointly reasons points and scene tokens, allowing information exchange across the point set.

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Published as a conference paper at ICLR 2026
Table 5: Ablations. All models are evaluated on the SCRREAM complete (K 1) setting, We
report CDJ, FS@0.11, FS@0.051 and FS FS@0.02f across different ablation settings.
Initial tokens (Stage
Scene tokens (Stage I)
FM Decoder (Stage I)
Img Resolution (Stage
Settings
Point
Learnable
Hybrid
256
512
768
Indep.
Joint
224
518
CD1
0.011
0.013
0.011
0.014
0.013
0.011
0.012
0,011
0.054
0,048
FS@0.11
0.999
0,998
0,999
0.996
0,998
0,999
0.998
0.999
0.861
0.882
F.
0.991
0
0,993
0.975
0,986
0.993
0.990
0.993
0.648
0.687
FS@0.021
0.894
0.841
0.904
0.811
0.839
0.904
0.889
0.904
0.327
0,350
Table 6: Ablations on different training loss functions. All models are evaluated on the SCR-
REAM complete (K 1) setting. We report CDL, FS 1S.011 FS FS@0.051 and FS FS@00.01 and infer-
ence time.. for the decoder.
SCRREAM (Stage 1)
Training Loss
CD 4
FS@0.1
FS@0.05 f FS@0.02
Inference Time (8) J
Chamfer distance
0.024
0.981
0.907
0.575
0,557
Flow-matching
0.011
0.999
0.993
0.904
2.985
To investigate the effect of this design, we compare it with an independent variant without self- attention. Empirically, the joint decoder yields lower CD, higher F-scores, and sharper fine details (Table 5), with small quantitative but significant qualitative gains (Figure 5).
Input image resolution (Stage 2). In Stage 2 (image-to-pol toint,,), we adopt a transformer to integrate information between image and scene tokens. The input resolution determines the number of image tokens used in the aggregation process. With patch size 14, resolution of 224 224 yields 16 16 256 tokens, while a resolution of 518 x 518 yields 37 x 37 1369 tokens. As shown in Table 5, training with 518 x 518 resolution consistently improves CD and F-scores.
Flow-matching loss vs. Chamfer distance loss. To verify the necessity of now-matching for unordered point cloud encoding, conduct an ablation using the same architecture but replacing the flow-matching loss with Chamfer Distance. Both models were trained on SCRREAM (Stage 1) under the same protocol. As shown in Table 6, flow-matching achieves significantly better recon- struction quality and generalization. Chamfer Distance struggles in scene-level settings because its nearest-neighhor formulation is computationally expensive, sensitive density imbalance, and un- able to capture global structure across varying scales and input views, while flow-matching produces stable, complete, and globally consistent reconstructions.
5 CONCLUSION
We present NOVA3R, non-pixel-alignee framework for amodal 3D reconstruction from posed images. Unlike prior pixel-aligned methods, our NOVA3R achieves state-of-the- results in amodal 3D reconstruction, including both visible and invisible points, both scene and object levels. Notably, it pioneers a new paradigm for physically plausible scene reconstruction that recon- structs uniform point cloud for the entire scene, without holes or duplicated points. This simple yet effective design makes it promising solution for real-world applications.
Limitations and Discussion. Due computational constraints, we train our model with a rela- tively small number of scene tokens and point clouds and moderate number of input views (up to 2). Hence, the reconstruction quality may degrade for large-scale scenes with complex structures. Future work can explore scaling up the model and training data to enhance performance and gen- eralization. In addition, our model currently focuses on reconstructing static scenes and does not handle dynamic objects or temporal consistency across frames.
ACKNOWLEDGMENTS
We would like to thank Ruining Li, Zeren Jiang, and Brandon Smart for their insightful feedback on the draft. This work was supported by the ERC Advanced Grant "SIMULACRON" (agree- ment #884679), the GNI Project "AI4Twinning", and the DFG project CR 250/26-1 "4DYoutube". Chuanxia Zheng is supported by NTU SUG-NAP and National Research Foundation, Singapore, under its NRF Fellowship Award NRF-NRFFI72025-0009
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REFERENCES
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies. Neural rgb-d surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6290-6301, 2022. 7, 8, 17, 18
Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin. Peter Fu, Thomas Gebauer, Brandon Joffe. Daniel Kurz, Arik Schwartz, and Elad Shulman. ARKitscenes diverse real- world dataset for 3d indoor scene understanding using mobile RGB-d data. In Thirty-fifth Con- ference Neural Information Processing Systems Datasets and Benchmarks Track, 2021. 7
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models large datasets. arXiv preprint arXi::231..15127 2023. 3
Alexey Bochkovskiy, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan Richter, and Vladlen Koltun. Depth pro: Sharp monocular metric depth in less than a second. In International Conference on Learning Representations (ICLR), 2025.
Mark Boss, Zixuan Huang, Aaryaman Vasishta, and Varun Jampani. Sf3d: Stable fast 3d mesh reconstruction with uv-unwrapping and illumination disentanglement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16240-16250, 2025. 9
Yohann Cabon, Naila Murray, and Martin Humenberger. Virtual kitti 2. arXiv preprint (rXi:20011.1773. 2020. 18
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-ricn rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 3
Jen-Hao Rick Chang, Yuyang Wang, Miguel Angel Bautista Martin, Jiatao Gu, Xiaoming Zhao, Josh Susskind, and Oncel Tuzel. 3d shape tokenization via latent flow matching. arXiv preprint 2rXiv:2412.15618 2024. 5,9
Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. Mvsplat360: Feed-forward 360 scene synthesis from sparse views. In Advances in Neural Information Processing Systems (NeurIPS), 2024.7 2,
Christopher B Choy, Danfei Xu, Jun Young Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In European conference on computer vision (ECCV), pp. 628-644. Springer, 2016.
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of anno- tated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13142-13155, 2023. 8
Laura Downs, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B McHugh, and Vincent Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In International Conference on Robotics and Automation (ICRA), pp. 2553-2560. IEEE, 2022.
David Eigen and Rob Fergus. Predicting depth, surface normals and semantic labels with common multi-scale convolutional architecture. In International Conference on Computer Vision (ICCV), pp. 2650-2658, 2015.
Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object recon- struction from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 605-613, 2017.
Haiwen Feng, Junyi Zhang, Qianqian Wang, Yufei Ye. Pengcheng Yu, Michael J. Black, Trevor Darrell, and Angjoo Kanazawa. St4rtrack: Simultaneous 4d reconstruction and tracking in the world. In International Conference Computer Vision (ICCV), 2025. 19
11

---

Published as a conference paper at ICLR 2026
Huan Fu, Bowen Cai, Lin Gao, Ling- Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al. 3dofront: 3d furnished rooms with layouts and semantics. In International Conference on Computer Vision (ICCV), pp. 10933-10942, 2021.
Ling-Xiao
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan Barron, and Ben Poole. Cat3d: Create anything in 3d with multi-view diffusion models. Advances in Neural Information Processing Systems (NeurIPS), 2024. 2,
Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning scalable 3d generative models from video diffusion models. In European Conference on Computer Vision (ECCV), pp. 333-350. Springer, 2024.
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In International Conference on Learning Representations (ICLR), 2024. 3
Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, and Shaojie Shen. Metric3d v2: A versatile monocular geometrie foundation model for zero-shot metric depth and surface normal estimation. IEEE Trunsuctions Patlern Analysis and Machine Intelligence, 2024." 7
Zixuan Huang, Mark Boss, Aaryaman Vasishta, James M Rehg, and Varun Jampani. Spar3d: Stable point-aware reconstruction of 3d objects from single images. In Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 16860-16870, 2025. 9
Hanwen Jiang, Hao Tan, Peng Wang, Haian Jin, Yue Zhao, Sai Bi, Kai Zhang, Fujun Luan, Kalyan Rayzer: A self-supervised large view synthesis model. In Sunkavalli, Qixing Huang, Computer Vision (ICCV), October 2025. 4 International Conference
HyunJun Jung, Weihang Li, Shun-Cheng Wu, William Bittner, Nikolas Brasch, Jifei Song, Eduardo Pérez-Pellitero, Zhensong Zhang, Arthur Moreau, Nassir Navab, et al. Serream: Scan, register, render and map: A framework for annotating accurate and dense 3d indoor scenes with a bench- mark. Advances Neural Information Processing Systems (NeurIPS), 37:44164-44176, 2024. 6, 7, 17
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. ACM Trans. Graph., 139-)
Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference Computer Vision (ECCV), pp. 71-91. Springer, 2024. 3
Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3D: Fast text-to-3D with sparse-view gen- cration and large reconstruction model. In The Twelfth International Conference on Learning Representations (ICLR), 2024. 3
Rui Li, Biao Zhang, Zhenyu Li, Federico Tombari, and Peter Wonka. Lari: Layered ray intersections for single-view view 3d geometric reasoning. arXiv preprint arXiv:2504.18424, 2025a. 6, 7, 8, 9, 16
Yangguang Li, Zi-Xin Zou, Zexiang Liu, Dehu Wang, Yuan Liang. Zhipeng Yu, Xingchao Liu, Yuan-Chen Guo, Ding Liang, Wanli Ouyang, et al. Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models. arXiv preprint arXiv:2502. 06608, 2025b. 2, 3, 5, 7, 16, 17
Yaron Lipman, Ricky Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference Learning Repre- sentations (ICLR), 2023.
Fangfu Liu, Wengiang Sun, Hanyang Wang. Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model, 2024. URL htttt:h////xxiiiie
67.7..7 2,
12

---

Published as a conference paper at ICLR 2026
Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One- 2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in 2023a.
Neural Information Processing Systems (NeurIPS).
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick.
1ero-1-to-3; Zero-shot one image to 3d object. In International Conference on Computer Vision (ICCV), pp. 9298-9309, 2023b.
Xiaoxiao Long. Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattem Recognition (CVPR), pp. 9970-9980 9980, 2024. 3
Mildenhall, PP Srinivasan, M Tancik, JT Barron, R Ramamoorthi, and R Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision (ECCV), 2020. 3
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European Conference Computer Vision (ECCV), 2012. 3
Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4209-4219 2024. 2, 3
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), pp.
2022. 3
Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Hermmann Hong- Ming Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. Zeronvs: Zero-shot 360-degree view synthesis from single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 0920-9429, 2024.
Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. MVDream: Multi-view difffusion for 3d generation. In The Twelfth International Conference on Learning Representations (ICLR), 2024. URL httttps:///peeeeeeee
net. wwwt/orumm derggiigguugggggbbab.... 3
Jamie Shotton, Ben Glocker, Christopher 7ach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 2930-2933321 2013. 7,8
Edgar Sucar, Zihang Lai, Fldar Insafutdinov, and Andrea Vedaldi. Dynamic point maps: A versatile representation for dynamic 3d reconstruction. In International Conference on Computer Vision (ICCV), pp. 7295-7305, October 2025. 19
Stanislaw Szymanowicz, Jason Y Zhang, Pratul Srinivasan, Ruiqi Gao, Arthur Brussee, Aleksander Holynski, Ricardo Barain-Brualla, Jonathan Barron, and Philipp Henzler. Bolt3d: Generating 3d scenes in seconds. In International Conference Computer Vision (ICCV), October 2025. 2,
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision (ECCV), pp. 1-18. Springer, 2025a. 3
Zhenggang Tang, Yuchen Fan, Dilin Wang, Hongyu Xu, Rakesh Ranjan, Alexander Schwing, and Zhicheng Yan. Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
June 2025b. 2.3
Tencent Hunyuan3D Team. Hunyuan3d 1.0: unified framework for text-to-3d 3d and image-to-3d generation, 2024. 2, 5
13

---

Published as a conference paper at ICLR 2026
Tencent Hunyuan3D Team. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation, 2025. 3,5
Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang. Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from single image. arXiv preprint Stti::2003.02155:. 2024. 2,3
Arash Vahdat, Francis Williams, Zan Gojeic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: La- tent point diffusion models for 3d shape generation. Advances in Neural Information Processing Systems (NeurIPS), 35:10021-10039 2022. 2,
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Informa- fion Processing Systems (NeurIPS), 30, 2017. 5
Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Chris- tian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. In European Conference on Com- puter Vision (ECCV), pp. 439-457. Springer, 2024.
Matthew Wallingford, Anand Bhattad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, and Ali Farhadi. From an image to a scene: Learn- ing to imagine the world from million 360 videos. Advances in Neural Information Processing Systems (NeurIPS), 37:17743-1776 2024. 3
Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pp. 5294-5306, 2025a. 2,3, 4, 6, 7, 8, 16
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. Pixel2mesh: Generating 3d mesh models from single rgb images. In European Conference on Computer Vision (ECCV), pp. 62-67,20 2018. 3
Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Con- tinuous 3d perception model with persistent state. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2025b. 2, 3, 7,8
Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, and Jiaolong Yang- Moge: Unlocking accurate monocular geometry estimation for open-domain images with opti- mal training supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5261-5271, 2025c.
Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Ge- ometric 3d vision made easy. In Proceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20697-20709, 2024a. 2, 4,7
Zhouxia Wang, Ziyang Yuan, Xintao Wang. Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Papers, pp. 1-11, 2024b.
Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul Srinivasan, Dor Verbin, Jonathan T Barron, Ben Poole, et al. Reconfusion: 3d reconstruction with diffusion priors. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21551-21561, 2024. 3
Tianhao Wu, Chuanxia Zheng, Frank Guan, Andrea Vedaldi, and Tat-Jen Cham. Amodal3r: Amodal 3d reconstruction from occluded 2d images. In International Conference on Computer Vision (ICCV), 2025. 4
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the IEÉE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2025a. 2,3, 17
14

---

Published as a conference paper at ICLR 2026
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 21469-21422440 2025b. 7,9
Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2025. 2,3
Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-l-to-3: Consistent im- age to 3d view synthesis via geometry-awane diffusion models. In 2024 International Conference on 3D Vision (3DV), pp. 664-674, IEEE, 2024. 3
Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high- fidelity dataset of 3d indoor scenes. In International Conference on Computer Vision (ICCV), pp. 12-22, 2023. 7
Hong-Xing Xing Yu, Haoyi Duan, Charles Herrmann, William T. Freeman, and Jiajun Wu. Wonderworld: Interactive 3d scene generation from a single image, Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), June 2025a. 3
Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewerafter: Taming video diffusion models for high- fidelity novel view synthesis. IEEE Transactions on Pattern Analysis & Machine Intelligence, (01):1-18, 2025b. 3
Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 1-16, 2023. 2, 5,
Kai Zhang. Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. Gs-Irm: Large reconstruction model for 3d gaussian splatting. In European Conference on Computer Vision (ECCV), pp. 1-19. Springer, 2024a. 3
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 43(4):1-20, 2024b. 3.3
Qihang Zhang, Shuangfei Zhai, Miguel Angel Bautista, Kevin Miao, Alexander Toshev, Joshua Susskind, and Jiatao Gu. World-consistent video diffusion with explicit 3d modeling. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025a. 4
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu, Nan Xue, Christian Rupprecht, Xiaowei Zhou, Yu- jun Shen, and Gordon Wetzstein. Flare: Feed-forward geometry, appearance and camera estima- tion from uncalibrated sparse views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2025b. 2,
Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang. Pei Cheng, Bin Fu, Tao Chen, Gang Yu, and Shenghua Gao. Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation. Advances in neural information processing systems (NeurIPS), 36: 73969-73982, 2023. 3
Chuanxia Zheng and Andrea Vedaldi. Free3d: Consistent novel view synthesis without 3d represen- tation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 9720-9731, 2024. 3
Jensen Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (CVPR), pp. 12405-12414, 2025.
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Published as a conference paper at ICLR 2026
A APPENDIX
A.1 MORE IMPLEMENTATION DETAILS.
Model architectures. For the 3D point autoencoder (Stage 1), follow the point encoder design from TripoSG Li et al. (2025b), which consists of one cross-attention layer and eight self-attention layers. The initial point queries are obtained by farthest point sampling from the input point cloud, while the learnable queries are randomly initialized tokens. We use 768 tokens with dimension 128 for the our model. For the flow-mate now-matching decoder, we use joint block with two cross-attention layers and one self-attention layer. The goal is to enable self reff-atteniion-lit information exchange among queries while keeping computation manageable. Concretely, each block (i) aggregates infor- mation from noisy query points into the scene tokens via cross-attenting (ii) performs self-attention on the scene tokens (small M) to mix global context efficiently, and (iii) projects the updated scene tokens back to the queries with a second cross-attention.
For the image-to-latent transformer in Stage 2, we follow the architecture of VGGT (Wang et al., 2025a), which alternates between local (frame-level) and global attention. Due to computational constraints, we adopt a 16-layer variant instead of the full 24-layer VGGT, initializing from its pretrained weights. We also reuse VGGT's image tokenizers with frozen weights to obtain image tokens. The initial 3D scene tokens are treated as a 3D frame and share the same local attention mechanism with the image tokens. For the 3D scene token, we copy the camera token from the first view to enable reconstruction in the camera coordinate of the first view.
Training. We train model in two stages. In Stage 1, aggregate per-view point clouds into a single input cloud and apply farthest point sampling on a randomly selected subset to supervise the now-matching decoder. Farthest point sampling ensures that the target point cloud is distributed more evenly, reducing the influence of overlapping points in visible regions. Stage 1 is trained for 50 epochs. In Stage 2, we reuse the flo-mmatching decoder from Stage and train together with our image encoder, initialized with pretrained VGGT weights. The same flow-mater wow-mathhing loss is used both stages. For object and scene completion, target point clouds are sampled from complete reconstructions. To demonstrate compatibility with pixel-aligned formats, we also train variant using RGB-D input, where target point clouds are sampled from point maps suck-projected from depth. Stage 2 is trained for another 50 epochs.
Regarding computational cost, the Stage-I point encoder is lightweight and requires no paired im- age-point cloud data, enabling efficient training on large-scale synthetic 3D datasets. In practice, Stage takes about 40% less training time than Stage 2, and inference remains single-stage, feed- forward, and efficient regardless of the two-stage setup. Overall, the two-stage design adds small overhead while substantially improving stability, data flexibility, and reconstruction quality.
Evaluation. For object- and scene-level completion, we follow Li et al. (2025a) and sample 10k points for the object task and 100k for the scene task. However, correspondence-bssee point cloud alignment not applicable due our -in-pixel-aligned reconstruction. Instead, we optimize 3D translation and a global (ID) scale relative to the ground-truth point cloud using Adam to im- prove alignment. We do not optimize rotation, as our reconstruction is expressed in the first-view coordinate frame
A.2 MORE ABLATION STUDY.
Input
25k
50k
100k
150k
Figure 9: Visualization of point cloud generation at different resolutions. Our non non-pixel-aligne formulation allows inference arbitrary resolutions.
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Published as a conference paper at ICLR 2026
Input (K=1)
VGGT
LaRI
Ours
GT
Qualitative results for scene completion on SCRREAM Jung et al. (2024), Our method shows better scene completion results compared to other baselines.
Figure 10:
Reconstruction at any resolution. Since NOVA3R models point distribution rather than a per- pixel point map, naturally supports resolution-agnostic generation by adjusting the number of noisy queries at inference. Figure 9 presents results with varying query counts for the flow-matching decoder. demonstrating that our method consistently produces point clouds at different resolutions with reliable reconstruction quality.
A.3 MORE VISUALIZATIONS.
We show more visualization results for scene-level completion on SCRREAM (Jung et al., 2024) dataset, as shonw in Figure 10. We also include density evaluation on NRGBD (Azinović et al., 2022) in Figure 11. While trained with K views only, our method generalize to multiple image views (K- = 4) and provides more evenly distributed point cloud.
A.4 REDUCING UNCERTAINTY IN LATENT DIFFUSION-BASED 3D GENERATION
Our method specifically designed reduce the uncertainty typically observed in latent diffu- sion-based 3D generation approaches such as TRELLIS (Xiang et al., 2025a) and TripoSG (Li et al., 2025b). These methods perform generation a high-dimensional latent space, which may lead to hallucinated geometry, shape deviations, and inconsistencies across multiple input images are involved. As result, they struggle to maintain strong pixel-to-scene and cross-view alignment.
when
In comparison, NOVA3R provides faithful reconstruction conditioned on the input images. Fur- thermore, NOVA3R can be integrated with the pretrained TRELLIS model to provide active voxel positions, effectively extending 3D object generation models to real-world scene synthesis without re-training (see Figure 12).
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Input (K=4)
CUT3R
VGGT
Ours
GT
Figure 11: Qualitative results for density evaluation on NRGBD (K=4) (Azinović et al., 2022).
Yellow regions denote higher density, and purple regions denote lower density. Our method provides more evenly-distributed point cloud (colored by density).
Input
TRELLIS
NOVA3R TRELLIS
Figure 12: Extending 3D object generation model to real-world scene reconstruction. The pre- trained TRELLIS model struggles to generate geometrically faithful reconstructions for real-world scenes. In contrast, NOVA3R can provide active voxel priors for the TRELLIS stage-1 generation process, enabling its extension to real-world scene synthesis.
A.5 PERFORMANCE ON OUTDOOR SCENES
To validate the robustness and generalization capability of our framework. further evaluate NOVA3R using the outdoor dataset Virtual KITTI 2 (Cabon et al., 2020). We finetune our model
Virtual KITTI 2 to better adapt to large-scale outdoor environments. To construct pseudo ground
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Published as a conference paper at ICLR 2026
Input (K-2)
Ours
Input (K-2)
Ours
Figure 13: Qualitative results for outdoor scenes reconstruction Virtual KITTI 2. Our
method is also applicable to outdoor scene reconstruction (colored by Y axis).
truth, each input frame we collect neighboring frames within [-4,8] timesteps and additional views from +15 and =30 viewpoints. Using depth maps and camera parameters, project them into per-frame point clouds, transform them to world coordinates, and retain only points within the target view's frustum. As shown in Figure 13, NOVA3R performs well outdoor scenes, further demonstrating its ability to handle both indoor and outdoor scenarios.
A.6 DISCUSSION
Large-scale Scenes. Modeling large-scale scenes with many input images is major compu- tational bottleneck for existing learning-based 3D reconstruction methods, particularly for pixel- aligned approaches like VGGT, which must handle duplicated points across multiple views. In con- trast, our point-wise decoding uses fewer tokens to represent the scene, making it inherently more scalable. However, the number of points needed varies across scenes of different scales, requiring adaptive point selection strategies, such as using sparse COLMAP point clouds as guidance.
Dynamic Scenes. Our paradigm is inherently extensible dynamic scenes, either by adding branch to predict target time point maps (Sucar et al., 2025; Feng et al., 2025) or by extending the 3D latent autoencoder to time-conditioned 4D latent representation. Such representation potentially model the entire 4D scene more efficiently by capturing both complete geometry and temporal evolution across the whole sequence, rather than relying on per-frame reconstruction.
19


Reviews:
[{"review_id": "t7fRNkysBL", "reviewer": "Reviewer_PSme", "summary": "This manuscript presents NOVA3R, a non-pixel-aligned visual transformer for amodal 3D reconstruction from unposed images. Unlike prior methods tied to pixel-level supervision, NOVA3R decouples geometry prediction from image pixels by learning a global scene representation. The approach leverages a diffusion-based 3D autoencoder trained with a flow matching loss, and introduces learnable scene tokens to aggregate multi-view information. The method demonstrates state-of-the-art performance across object-level and scene-level benchmarks, outperforming both single- and multi-view baselines.", "questions": "- While the method focuses on amodal 3D reconstruction, do you have any thoughts on how image-based properties such as camera poses or intrinsics, or depth maps can be derived from the proposed scene representation?\n- How does the method generalize to more views that covers larger areas of the scene? e.g. how the method performs on autonomous driving datasets such as nuScenes or Waymo?\n- What is the maximum pose gap between the two input images? How large the portion of the scene can be completed?\n- How can the method be extended to handle dynamic scenes? How can the method handle incremental reconstruction?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written and well-presented. The provided figures are clear and informative. The accompanying video shows clearly the method in action.\n- The idea of predicting non-pixel-aligned geometry is novel and interesting. While the literature of 3D generation is rich, framing the tasks as an intersection of DUST3R-style prediction and 3D-native prediction is clever and refreshing.\n- The method achieves state-of-the-art performance both on the SCRREAM dataset and NRGBD/7-Scenes dataset, demonstrating the effectiveness of the proposed method.", "weaknesses": "- The runtime performance of the flow-matching decoder is not analyzed. It would be nice to show how the proposed method compares to a regular point cloud decoder without the diffusion process.\n- It is unclear how image-based properties, such as camera poses or intrinsics, or depth maps, can be derived. While the entire scene is put under the coordinate system of the first view, it is not clear how accurate it is for other views. Pose or depth accuracy evaluation is not provided.\n- It is not clear how the proposed method can be extended to more than 4 views, i.e., it is not clear how a global bundle adjustment problem can be formulated as in DUST3R to incorporate videos of arbitrary length.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xAeEr7jKVz", "reviewer": "Reviewer_3fs2", "summary": "This paper proposes NOVA3R, a non-pixel-aligned 3D reconstruction method that effectively improves occluded region completion through a scene-token mechanism and flow-matching loss. It provides a feasible solution for the non-pixel-aligned paradigm.", "questions": "1. Innovation Needs Further Quantification. The core innovation of the paper lies in the synergy between the non-pixel-aligned paradigm and Scene Token. It is recommended to quantify the token’s contribution to global structure modeling. \n2. Qualitative Results Require Objective Quantitative Support. The core goal of qualitative experiments (Figures 6, 7) is to verify \"occlusion completion\" and \"physical plausibility,\" but current evaluations rely solely on visual judgment, leading to potential subjectivity in assessing effectiveness. It is suggested to supplement quantitative metrics (e.g., hole area ratio, point cloud density variance) to objectively measure differences between NOVA3R and baselines. Furthermore, enrich qualitative results for complex scenes to highlight the method’s advantages in challenging scenarios.\n3. Validation Scope for Scene Completion Needs Expansion. Verification is only conducted under  (single view) and  (two views), which is insufficient to fully demonstrate the gain of multi-view input for complete reconstruction. It is recommended to add experiments to analyze the relationship between the number of views and reconstruction completeness  as well as physical plausibility.\n4. Comparative Experiments Need Clear Task Boundaries and Explanations for Omitted Comparisons. When comparing with pixel-aligned methods (e.g., DUSt3R, VGGT), it is necessary to more explicitly explain the \"impact of task differences on metrics\" (e.g., VGGT outperforms in CD because it does not need to handle occlusion completion). For the omission of Amodal3R in Object Completion experiments, supplement explanations for the exclusion (e.g., Amodal3R requires mask input and only works for object-level reconstruction). If comparisons are deemed necessary, design adaptive experiments (e.g., providing NOVA3R with the same masks as Amodal3R) before conducting comparisons.\n5. Analysis of Flow-Matching Loss Needs Deepening. The paper only mentions using flow-matching loss to address unordered point cloud supervision but fails to analyze its advantages over traditional losses. It also does not explain the tuning strategy for loss hyperparameters (e.g., sampling strategy for time step ). It is recommended to add ablation experiments comparing \"different loss functions\" to verify the necessity of flow-matching for unordered point cloud encoding. Additionally, explain the tuning differences of the loss across datasets.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "As a non-pixel-aligned 3D reconstruction framework, NOVA3R decouples reconstruction from pixel-ray binding via a global Scene Token mechanism. It completes occluded regions, addressing the flaw of traditional pixel-aligned methods (e.g., DUSt3R, VGGT).", "weaknesses": "1. Innovation Needs Further Quantification. The core innovation of the paper lies in the synergy between the non-pixel-aligned paradigm and Scene Token. It is recommended to quantify the token’s contribution to global structure modeling. \n2. Qualitative Results Require Objective Quantitative Support. The core goal of qualitative experiments (Figures 6, 7) is to verify \"occlusion completion\" and \"physical plausibility,\" but current evaluations rely solely on visual judgment, leading to potential subjectivity in assessing effectiveness. It is suggested to supplement quantitative metrics (e.g., hole area ratio, point cloud density variance) to objectively measure differences between NOVA3R and baselines. Furthermore, enrich qualitative results for complex scenes to highlight the method’s advantages in challenging scenarios.\n3. Validation Scope for Scene Completion Needs Expansion. Verification is only conducted under  (single view) and  (two views), which is insufficient to fully demonstrate the gain of multi-view input for complete reconstruction. It is recommended to add experiments to analyze the relationship between the number of views and reconstruction completeness  as well as physical plausibility.\n4. Comparative Experiments Need Clear Task Boundaries and Explanations for Omitted Comparisons. When comparing with pixel-aligned methods (e.g., DUSt3R, VGGT), it is necessary to more explicitly explain the \"impact of task differences on metrics\" (e.g., VGGT outperforms in CD because it does not need to handle occlusion completion). For the omission of Amodal3R in Object Completion experiments, supplement explanations for the exclusion (e.g., Amodal3R requires mask input and only works for object-level reconstruction). If comparisons are deemed necessary, design adaptive experiments (e.g., providing NOVA3R with the same masks as Amodal3R) before conducting comparisons.\n5. Analysis of Flow-Matching Loss Needs Deepening. The paper only mentions using flow-matching loss to address unordered point cloud supervision but fails to analyze its advantages over traditional losses. It also does not explain the tuning strategy for loss hyperparameters (e.g., sampling strategy for time step ). It is recommended to add ablation experiments comparing \"different loss functions\" to verify the necessity of flow-matching for unordered point cloud encoding. Additionally, explain the tuning differences of the loss across datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3EYBX9eneQ", "reviewer": "Reviewer_Fedf", "summary": "NOVA3R is a non-pixel-aligned visual Transformer approach for feed-forward amodal 3D reconstruction from unposed images. It addresses the limitations of pixel-aligned methods—such as only reconstructing visible regions and generating duplicated structures in overlapping areas—and overcomes the inability of latent 3D generation methods to handle complex scenes by learning a global, view-agnostic scene representation.\n\nThe core of this method includes a scene-token mechanism (to aggregate multi-view information) and a diffusion-based 3D decoder (to generate complete point clouds). It adopts a two-stage training process: first, a 3D latent autoencoder is trained using a flow-matching loss; then, a pre-trained image encoder and learnable scene tokens are used to map images to the latent space.\n\nExperiments show that NOVA3R outperforms state-of-the-art methods on both scene-level (SCRREAM, 7-Scenes) and object-level (GSO) datasets, producing more complete and physically plausible reconstruction results. It supports both single-view and multi-view inputs, combining feed-forward efficiency with strong modeling capabilities. Its limitations include insufficient reconstruction quality for large-scale complex scenes and no support for dynamic objects.", "questions": "Regarding the paper proposing NOVA3R, there are three key questions to raise with the authors, corresponding to potential weaknesses: First, compared with end-to-end structures, the two-stage training design requires additional training efforts, and it is worth discussing whether the training complexity can be optimized. Second, the paradigm of \"compression-decompression\" plus diffusion-based decoding may bring uncertainty to the consistency between the reconstructed point cloud and the input image, and further verification of this consistency is needed. Third, the point-wise decoding nature makes it difficult for the model to scale to scenarios with an extremely large number of input images, as the increase in point cloud quantity will lead to significant computational bottlenecks.\n\nNevertheless, integrating the generative idea into the VGGT series of works is an excellent and valuable concept. Although a hybrid framework (using VGGT for visible regions and the proposed method for invisible regions) might perform better than solely adopting NOVA3R, the framework proposed in this paper still has significant reference value. Therefore, it is recommended to give this paper a borderline accept decision.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. By modeling the entire 3D reconstruction task as a two-stage generative process, NOVA3R breaks the traditional pixel-aligned paradigm that estimates geometric attributes by tying geometry to per-ray predictions. This innovative design decouples reconstruction from pixel alignment, enabling the model to learn a global, view-agnostic scene representation and thus reconstruct point clouds of both visible and invisible (occluded) regions, addressing the incompleteness limitation of pixel-aligned methods .\n\n2. NOVA3R innovatively adopts diffusion as the point cloud decoder, which is highly suitable for the inherent characteristics of point clouds (e.g., unordered and unaligned nature). Unlike deterministic decoders that struggle with matching unordered point sets, the diffusion-based decoder, trained with a flow-matching loss, effectively resolves matching ambiguities in unordered point clouds, ensuring the generation of complete, physically plausible non-pixel-aligned point clouds without duplicated structures.\n\n3.experiments", "weaknesses": "1. Compared with end-to-end architectures, NOVA3R requires additional training efforts due to its two-stage design. The model first trains a 3D latent autoencoder (Stage 1) with a flow-matching loss to compress complete point clouds into latent tokens and decode them, then optimizes the image encoder and learnable scene tokens (Stage 2) to map unposed images to the latent space. This two-stage training not only increases the overall training pipeline complexity but also demands more computational resources and time compared to one-step end-to-end reconstruction methods.\n\n2. Concerns arise regarding the consistency between the reconstructed point clouds and input images under NOVA3R’s paradigm. Unlike pixel-aligned methods that directly predict geometry attributes (e.g., depth, point maps) tied to image pixels, NOVA3R undergoes a \"compression-decompression\" process: it compresses scene information into latent tokens and decompresses them via a diffusion-based generative decoder. This indirect mapping, combined with the stochastic nature of diffusion, introduces uncertainties about whether the generated non-pixel-aligned point clouds can fully align with the visual content of input images, requiring further validation of cross-view and pixel-scene consistency.\n\n3. This modeling approach may struggle to scale to scenarios with an extremely large number of input images, primarily due to computational bottlenecks caused by its point-wise decoding nature. As the number of point clouds required for reconstructing complex, large-scale scenes increases (e.g., when processing more input views to enhance reconstruction completeness), the diffusion-based decoder— which operates on individual points during the decompression process—faces a significant surge in computational load. Unlike pixel-aligned methods that can leverage per-ray parallelization tied to image pixels, NOVA3R’s non-pixel-aligned, point-wise decoding lacks efficient scalability for handling massive point volumes, making it challenging to adapt to tasks requiring extensive multi-view input or high-density point cloud output.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BMCMMgtTSZ", "reviewer": "Reviewer_t2bA", "summary": "This paper introduces NOVA3R, a novel feed-forward framework for amodal 3D reconstruction from a set of unposed images. The central problem the authors address is the limitations of traditional \"pixel-aligned\" methods (like DUSt3R or VGGT), which are tied to per-ray predictions. NOVA3R's core idea is to decouple reconstruction from pixel alignment by learning a global, view-agnostic scene representation. This allows the model to recover both visible and occluded points and generate a single, physically plausible point cloud for the entire scene. The method is implemented as a two-stage pipeline, the first is 3D Latent AutoEncoder training and the second one is Feed-forward Scene Prediction. Experiments on both scene-level (SCRREAM, NRGBD) and object-level (GSO) datasets show that NOVA3R outperforms state-of-the-art methods in reconstruction completeness and accuracy.", "questions": "1. In stage1's diffusion decoder training, how do you matches the unordered points in flow matching?\n2. Any experiments on outdoor scenes?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The paper's main strength is its \"non-pixel-aligned\" formulation. This is a clear and well-motivated departure from dominant pixel-aligned methods. It directly addresses fundamental, known issues in 3D reconstruction (incompleteness and duplicate geometry).\n2. The architectural design is novel and effective. Using a diffusion decoder with a flow-matching loss elegantly transforms noisy point clouds to target distribution with conditioning.\n3. The experimental validation is thorough. NOVA3R demonstrates superior performance over strong baselines in its primary goal of complete amodal reconstruction (Table 1) and also shows it can be applied to object-level tasks (Table 3).\n4. The paper shows that this new paradigm is also efficient. NOVA3R has significantly fewer parameters (718M) than both the pixel-aligned VGGT (941M) and the latent-generation TRELLIS (1785M) models.", "weaknesses": "1. While the method commendably avoids the need for high-quality meshes, the autoencoder in Stage 1 still requires \"complete point clouds\" for supervision. The authors' solution is to aggregate depth maps from dense views. However, it still can not eliminate the ambiguity of occluded or invisible area.\n2. The point cloud is noisier compared to VGGT. VGGT presents a smoother plane in Figure 6. This is also reflected in normal consistency metrics.\n3. Static scene only. It seems like this paradigm is hard to generalize to dynamic scenes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)