YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting
Authors: Botao Ye
Boqi Chen
Haofei Xu
Daniel Barath
Marc Pollefeys
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian splatting
feedforward model
novel view synthesis
pose-free
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Fast and flexible 3D scene reconstruction from unstructured image collections remains a significant challenge. We present YoNoSplat, a feedforward model that reconstructs high-quality 3D Gaussian Splatting representations from an arbitrary number of images. Our model is highly versatile, operating effectively with both posed and unposed, calibrated and uncalibrated inputs. YoNoSplat predicts local Gaussians and camera poses for each view, which are aggregated into a global representation using either predicted or provided poses. To overcome the inherent difficulty of jointly learning 3D Gaussians and camera parameters, we introduce a novel mixing training strategy. This approach mitigates the entanglement between the two tasks by initially using ground-truth poses to aggregate local Gaussians and gradually transitioning to a mix of predicted and ground-truth poses, which prevents both training instability and exposure bias. We further resolve the scale ambiguity problem by a novel pairwise camera-distance normalization scheme and by embedding camera intrinsics into the network. Moreover, YoNoSplat also predicts intrinsic parameters, making it feasible for uncalibrated inputs. YoNoSplat demonstrates exceptional efficiency, reconstructing a scene from 100 views (at 280×518 resolution) in just 2.69 seconds on an NVIDIA GH200 GPU. It achieves state-of-the-art performance on standard benchmarks in both pose-free and pose-dependent settings.  Our project page is at https://botaoye.github.io/yonosplat/.

Published as a conference paper at ICLR 2026
YONOSPLAT: YOU ONLY NEED ONE MODEL FOR FEEDFORWARD 3D GAUSSIAN SPLATTING
Botao Ye!.2
Boqi Chen
Haofei Xu'
Daniel Barath
Mare Pollefeys
ETT Zurich
2TTH Al Center
Microsoft
N Images
Feedforward
PR
Predicted Input Views
Novel Views
Optional:
3D Gaussians
Intrinscss
Rendered Novel Views
Rendered Depth
Figure 1: YoNoSplat, a versatile feedforward model for rapid 3D reconstruction. Given an arbitrary number of unposed and uncalibrated multi-view images with wide spatial coverage, predicts 3D Gaussians and can also utilize ground-truth camera poses or intrinsics when available.
ABSTRACT
Fast and flexible 3D scene reconstruction from unstructured image collections re- mains significant challenge. We present YoNoSplat, feedforward model that reconstructs high-quality 3D Gaussian Splatting representations from an arbitrary number of multi-view images. Our model is highly versatile, operating effec- tively with both posed and unposed, calibrated and uncalibrated inputs. YoNoS- plat predicts local Gaussians and camera poses for each view, which are aggre- gated into a global representation using either predicted provided poses. To overcome the inherent difficulty of jointly learning 3D Gaussians and camera parameters, we introduce novel mixing training strategy. This approach mit- igates the entanglement between the two tasks by initially using ground-truth poses aggregate local Gaussians and gradually transitioning to mix of pre- dicted and ground-truth poses, which prevents both training instability and expo- sure bias. We further resolve the scale ambiguity problem by novel pairwise camera-distance normalization scheme and by embedding camera intrinsics into the network. Moreover, YoNoSplat also predicts intrinsic parameters, making it feasible for uncalibrated inputs. YoNoSplat demonstrates exceptional efficiency, reconstructing a scene from 100 views (at 280x518 resolution) in just 2.69 seconds on NVIDIA GH200 GPU. It achieves srate-ofthhe-- performance standard benchmarks in both pose-free and pose-dependent settings. Our project page botaoye. hitaoye.gituub.ioyyooospppap
INTRODUCTION
Feedforward Gaussian Splatting (Charatan al. 2024 Zhang et al. 2025a) has emerged as promising direction for accelerating 3D scene reconstruction, directly predicting 3D Gaussian pa- rameters (Kerbl et al. 2023) from input images. This approach bypasses the time-consuming per- scene optimization required by methods like NeRF (Mildenhall al. 2020) and the original 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023). However, the real-world applicability of existing feedforward models is often constrained by restrictive assumptions, such the need for accurate camera poses (Charatan et al. 20224) Xu et al. 2025), calibrated intrinsics (Ye et al.] 2025 Zhang al. 2025b), fixed, limited number of input views Ye et al. 2025 Hong et al. 2024a)

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Pose-Dependent
Pose-Free
Pose-Dependent
Pose-Free
Pose-Dependent
Pose-Free
Rendering
Feror Map
(a) Self-forcing Training
(b) Teacher-forcing Training
(c) Mix-forcin Training
Figure 2: Effeet of different global Gaussian aggregation strategies during training. (a) Ag- gregating global Gaussians with predicted camera poses results in poor rendering quality because errors in pose estimation and Gaussian learning compound each other. (b) Using ground-truth poses introduces exposure bias (as indicated by the green arrow: training with ground-truth poses but test- ing with predicted poses causes misalignment of local Gaussians across different views). (c) Our mix-forcing training achieves high rendering quality in both pose-free and pose-dependent settings.
In practice, scene reconstruction needs to operate under flexible and unconstrained conditions: cam- poses may be unavailable or noisy, camera intrinsics unknown, and the number of images may vary significantly. Designing a single model that generalizes across these diverse settings - varying number of views, posed or unposed, calibrated or uncalibrated remains open challenge.
In this work, we introduce YoNoSplat, a feedforward model that reconstructs 3D scenes from an arbitrary number of unposed and uncalibrated images, while also seamlessly integrating ground- truth camera information when available. Recent pose-free methods (Ye et al. (2055: Smart et al.) 2024) have shown impressive results sparse inputs (2-4 views) by predicting Gaussians directly into a unified canonical space. However, this approach struggles to scale to large numbers of views. To ensure scalability and versatility, YoNoSplat adopts different paradigm: it first predicts per- view local Gaussians and their corresponding camera poses, which are then aggregated into a global coordinate system.
This local-to-gue to-gloobl design, however, introduces a significant training challenge: the joint learning of camera poses and 3D geometry is highly entangled. Errors in pose estimation can corrupt the learning signal for the Gaussians, and vece-versa. A naive approach that aggregates Gaussians using the model's own predicted poses, known as the self-forcing mechanism (Huang et al., 2025). leads to unstable training and poor performance (Fig. 2) Conversely, prior methods such as CoPoN- cRF Hong et al. 2024b) adopt a teacher-forcing strategy that relies solely on ground-trute -ruth poses for aggregation, which decouples the tasks but also introduces exposure bias (Ranzato et al. 2015). In this case, the model is never trained on its own imperfect pose predictions, causing performance to degrade at inference when it must depend them (Fig. (2p). To resolve this dilemma, propose a novel mix-forcing training strategy. Training begins with pure teacher-forcing to establish a stable geometric foundation. As training progresses, we gradually introduce the model's predicted poses into the aggregation step. This curriculum balances stability with robustness, enabling YoNoSplat to operate effectively with either ground-truth or predicted poses test time (Fig. 2b).
A second fundamental challenge is scale ambiguity, which is particularly pronounced when ground- truth depth is unavailable. This ambiguity arises from two sources: training data poses are often defined only up to an arbitrary scale, and jointly estimating intrinsics and extrinsics is an ill-posed problem without consistent scale reference. Inspired by NoPoSplat (Ye et al.) (025). which high- lighted the importance of camera intrinsics for scale recovery, we develop a pipeline that not only uses intrinsic information but also predicts it, enabling reconstruction from uncalibrated images. To address the data-level ambiguity, we systematically evaluate several scene-normalee normalizatin strategies and find that normalizing by the maximum pairwise camera distance is most effective, as it aligns with the relative pose supervision used during training (Wang al. 2025c).
Extensive experiments show that our model, even without ground-truth camera inputs, outperforms prior pose-dependent methods, highlighting the strong geometric and appearance priors learned through our training strategy. The approach generalizes across datasets and varying numbers of views, and reconstructs a complete 3D scene from 100 images in just 2.69 seconds.
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Our main contributions are follows:
We introduce YoNoSplat, the first feedforward model to achieve state-of-the-ar performance in both pose-free and pose-dependent settings for arbitrary number of views.
We identify the entanglement pose and geometry learning as a key challenge and propose a mix-forcing training strategy that effectively mitigates training instability and exposure bias.
We resolve the scale ambiguity problem through an intrinsic- inriisic-prediction-nie prtdi--irodddddd-cooooee conditioii pipeline and pairwise distance normalization scheme, enabling reconstruction from uncalibrated images.
RELATED WORK
Feedforward 3DGS and NeRF Original NeRF (Mildenhall et al. 2020), 3DGS (Kerbl et al. 2023
and their variants (uülle et al. 2022 Barron et al. 2021| Ye et al. 2023) require time-consuming
per-scene optimization. To address this mefficiency, numerous feedforward methods (Yu et al, 2021 Hong et al., 2024c) Zhang et al. 2025a Charatan et al. 2024, Chen et al., 2024) have been proposed. These approaches train neural networks large-scale datasets to learn geometric and appearance priors, enabling generalization to novel scenes, However, they typically require precise camera poses as input and are restricted small number of input views (usually 2-4).
Several works relax individual constraints. For instance, Long-LRM (Zien et al. 2025) and Depth- Splat (Xu al. 2025) reconstruct scenes from multiple input images through feedforward net- work, but still rely on accurate camera poses. Recent free methods (Ye et al. 2025 Zhang al., (0255) can reconstruct baussian-based scenes from unposed images and even outperform pose-dependent counterparts (Charatan et al. 2024, Chen et al.) 2024). Yet, they focus primarily on dual-view inputs; while extendable more views, they remain limited scenes with sparse coverage. These methods require known intrinsics and operate with a fixed number of views.
In contrast, our work addresses all these challenges simultaneously: we predict local 3D Gaussians, camera intrinsics, and poses feedforward from an arbitrary number of unposed images. The most similar effort the concurrent AnySplat (Jiang et al. 2025). However, AnySplat cannot leverage available priors such as intrinsics extrinsics, whereas our method flexibly incorporates them when present. Furthermore, through a carefully designed training paradigm and pose-normalization strat- egy, YoNoSplat achieves substantially stronger performance.
Feedforward Point Cloud Prediction. Another line of work closely related to ours is feedfor- ward point cloud prediction models (Wang et al., 2024 2025a DUSt3R (Wang al.) 2024)
demonstrated that a feedforward model trained on large-scale datasets can accurately predict cam- intrinsics and scene geometry without requiring optimization. Subsequent works extended this idea to a larger number of input views (Wang et al., [225al Yang et al. 2025; Wang et al.) 2025ch and to incremental feedforward reconstruction (Wang al.J (025b). However, these methods cannot be applied to novel view synthesis due to the discontinuous nature of point clouds. Moreover, they all require ground-truth depth supervision during training. In contrast, by modeling 3D Gaussians as the output representation, YoNoSplat supports both novel view synthesis and effectively utilizes datasets that lack ground-truit depth, such as RealEstate 10K Zhou et al. (2018).
3 METHOD
We introduce YoNoSplat, method for the feedforward prediction of 3D Gaussians from multiple images. Our approach supports a wide range of scene scales and can optionally utilize available ground truth camera poses and intrinsics.
Problem Formulation. Given V unposed images (I") as input, where I" e 3 our objective is to learn feedforward network @ that predicts 3D Gaussians representing the underly- ing scene. By learning geometric and appearance priors from the training data, our method directly reconstructs new scenes without the need for time-consuming optimization. YoNoSplat do this by first predicting per-view local 3D Gaussians that can be transformed into global scene repre- sentation using the given or predicted camera poses p". Specifically, the camera pose parameters defined as p" [R", t=], where R" g denotes the rotation matrix, t" R represents the translation vector, and [-] indicates the concatenation operation. Furthermore, as described in Sec. 3.2 network also predicts the camera intrinsics k", thus also eliminate the requirement
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Intrissio Token
ICE
Local-Ciobal Attetion
Gausiian
Heads
Impul Views
ICFFF
LE-61
DINO
ICB
Ray Embesking
Pose
Haid
Global 3D Gaussians
Linear
(6) Overall Pipeline
(b) ICE Module
Figure 3: Overview of YoNoSplat. (a) Features are extracted with DINOv2 encoder, followed by local-global attention across images, and finally used to predict camera poses and local 3D Gaus- sians. (b) The Intrinsic Condition Embedding (ICE) module predicts intrinsic parameters (i.e., focal length), which are then converted into camera rays and re-encoded as conditioning for Gaussian prediction, thereby resolving scale ambiguity.
of camera calibration. Formally, aim to learn the following mapping:
fo (((( {u (p). aj ry. sj, k., p") v=
(1)
j=1..., HxW
Here, (Mi; Ozj ry 8j, Cj) denote Gaussian parameters (Kerbl et 2023 representing the center position, opacity, rotation, scale, and color, respectively. All parameters are initially predicted in the local input camera views and can subsequently be transformed into a global representation using either predicted or given camera poses.
3.1 ANALYSIS OF THE GAUSSIAN OUTPUT SPACE AND TRAINING STRATEGY
Output Space: Local vs. Canonical Prediction. A fundamental design choice for a feedforward reconstruction model is its output space. Existing methods fall into two main categories. Pose- free models such as NoPoSplat Ye et al. 2025) and Flare Zhang et I (2025b) predict Gaussians directly in a unified canonical space, which naturally aligns the outputs from all views into shared coordinate system. In contrast, desendepend methods like pixelSplat (Charatan et al. (2024) and MVSplat (Chen et al. 2024) predict Gaussians local, per-view space and rely on ground-truth camera poses to transform them into the global world frame.
While aanonical-space prediction is effective for a small number of views, its performance degrades as the view count increases (see Tab. 7 an observation consistent with findings in related feed- forward point-cloud prediction models (Wagg et al. (025a)). To ensure scalability and versatility, YoNoSplat adopts a local prediction paradigm. We architect our model to predict per-view local Gaussians alongside their corresponding camera poses. This design enables our primary goal of pose-free reconstruction by using the predicted poses for aggregation, yet it also retains full compat- ibility with pose-dependent workflows where ground-truth poses can be supplied. This flexibility is critical for real-world applications, such map reconstruction, where alignment with a pre-existing. accurate pose distribution is required.
Training Strategy: Mitigating Pose Entangiement. Jointly predicting 3D Gaussians and camera parameters is challenging, as errors in one corrupt the other. Using only predicted poses for aggre- gation (self-foreing Huuagg et (2025) tightly couples the tasks, leading to unstable training and degraded performance (Fig. 2 Tab. 5). Using only ground-truth poses (teacher-forcing (Williams) & Zipser, 1989)) provides a stable signal but causes exposure bias Ranzato al.) (2015), since the model never trains on its own imperfect predictions. To resolve this dilemma, we introduce a novel mix-forcing training strategy that combines the benefits of both approaches. Our training curriculum begins by exclusively using ground-truth poses feeacer-ffrcingg to allow the model to learn stable geometric foundation. After a predefined number of steps, sstrtt the probability of using the model's predicted poses for aggregation is linearly increased, eventually reaching final mixing ratio step fend- This strategy effectively mitigates entanglement by first establishing strong prior for the 3D structure and then gradually adapting the model both its own predicted distribution and the ground-truth pose distribution, thereby preventing training instability and exposure bias.
3.2 MODEL ARCHITECTURE
The overall architecture YoNoSplat is shown in Fig. 3 We build upon a Vision Transformer (ViT) backbone (Dosovitskiy al. 2021) and employ local-global attention mechanism in
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VGGT (Wang al. 2025a) for robust multi-view feature fusion, which scales more effectively with a large number of input frames than the cross-attention used in prior works (Ye et al. 2025).
Backbone Network. Input images (I") are divided into patches and flattened into tokens. These image tokens are concatenated with a learnable camera intrinsic token and processed by a ViT en- coder with DINOv2 architecture (Oquab al. 2023). The encoded features then pass through decoder consisting of alternating attention blocks (Wang et al. 2025a ch. Each block contains a per-frame self-attention layer for local feature refinement and global concatenated self-attention layer where tokens from all views are combined to facilitate cross-frame information flow.
Gaussian Heads. Following (Ye et al. 2025
Two separate heads predict the Gaussian centers and all other parameters. Each head consists of . self-attention layers and final linear layer. To capture fine-grained detail, we upsample the backbone features by factor of two before feeding them to the heads and add skip connection from the input image to combat information loss from the ViT's downsampling.
Pose Head. As discussed in Sec. 3.1 YoNoSplat first predicts local Gaussian parameters and then uses either the given or predicted camera poses to transform them into a unified global coordinate system. The camera head consists of an MI.P layer, followed by average pooling and another MIP, to predict a 12D camera vector following (Dong et al., 2025) Wang et al. al.] (025c). This output vector includes the camera translation t" and a 9D rotation representation (Levinson et al. 2020). which is converted into R" using SVD orthogonalization. During training, we follow x Wang et al. (2025c)
and supervise the camera pose with pairwise relative transformation loss (see Sec. (.44, ensuring that our model remains invariant to the order of input images.
Intrinsic Head. Predicting camera poses requires cross- cross-frame information and is thus performed during the decoder stage. In contrast, predicting camera intrinsics can be inferred from individual images. Therefore, we perform intrinsic prediction during the encoder stage. Additionally, con- ditioning on camera intrinsics helps resolve the scale ambiguity problem, as detailed in Sec. 3.3 Specifically, we concatenate an intrinsic token with the input image tokens, which are then pro- cessed by the encoder network, allowing the intrinsic token to aggregate image information. This token subsequently passed through an MLP layer to predict the camera intrinsics.
3.3 RESOLVING SCALE AMBIGUITY
Learning to predict Gaussians from video data encounters a scale ambiguity problem, arising from two main factors: (1) training datasets often provide SfM-derived camera poses that are only defined up to an arbitrary scale, and (2) jointly learning camera intrinsies and extrinsics an ill-posed problem. We address both factors.
Scene Normalization. The ground- ground-truth poses in our training datasets (Zhou al. 2018 Ling et al.
2024) are obtained using SIM methods (Schonberger & Frahm 2016), which are only defined up to scale. To avoid scale ambiguity that could hinder learning, it is therefore necessary to normalize the scene during training. Some plint prediction methods (Wang al. 2024 2025a) normalize scenes using ground-trus -ruth depth, but this is not fcasible for datasets without depth annotations.
To address this, we propose and evaluate three normalization strategies:
1. Max pairwise distance: given camera centers {c compute de = =Icc - -cyll2. and set max,,, daj, then normalize é; ca/s.
2. Mean pairwise distance: use NNN-I] E Eigs ||e
cy l2 and normalize as è ci/s.
Max translation: set s = max; ||c||22 and normalize & = c//8.
As shown in Tab. 6 max pairwise distance normalization performs best and is critical to the model's success. Since we employ relative camera poses, normalizing by the maximum pairwise distance ensures consistent scale for camera translations during training.
Intrinsic Condition Embedding (ICE). As demonstrated in (Ye et al. 2025), camera intrinsic in- formation is crucial for resolving scale ambiguity. However, prior work required ground-truth intrin- sics at inference time. To remove this dependency, we introduce our Intrinsic Condition Embedding first predicted after the encoder stage
(ICE) module (Fig. 33). Specifically, intrinsic parameters using the initial intrinsic token, as detailed in See 3.2 Subsequently, to implement intrinsic condi- tioning, the predicted parameters transformed into camera rays (Ye et al. 2025), passed through
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24 View
12
View
GT
Ours
AnySplat
NoPoSplat
MVSplat
DepthSplat
InstantSplat
Figure 4: Qualitative comparison DL3DV (Ling et al. 2024). Here we present results
in the pose-free, calibration-free setting, which still produce higher-quality novel view renderings compared to the pose-dependent method DepthSplat (Xu et al. 2025).
linear layer to obtain embedding features, and then added to the original image features. When bround-truth intrinsics are available, we use them directly to provide more accurate conditioning and thus achieve better performance. In their absence, we instead use the predicted intrinsics for network conditioning. Notably, during training, we condition the network on ground-truth intrinsics rather than the predicted ones. We also experimented with conditioning the decoder on intrinsics predicted by the encoder, but this led to training instability and eventual failure.
3.4
MODEL TRAINING
Our models are trained with multi-task loss as follows:
L = Limuge + Dinarin Cintrin + Axuse C + Aupacity Sepucity.
2025), the rendering loss Limage is set as linear combination of Mean Squared Error (MSH) and LPIPS (Zhang et al. 2018) loss, which is employed to optimize the Gaussians. During training, we randomly sample 4 target views and render the corresponding images using their ground truth camera poses, and then the rendered images are compared against the ground truth image.
Rendering Loss. Following previous works (Chen et al. 2024 Ye et al.
Intrinsic Loss. The intrinsic loss Lintria is used to train the intrinsic prediction head, which is the la distance between the predicted focal length with the ground truth.
Pose Loss. Following Wang al. (2025c), tive pose loss. Specifically, for each pair of input views (i,j) and their predicted pose (p.,
supervise the pose prediction head with rela-
we first calculate their relative pose Pit-j = p 'p.. The loss is then calculated as Lpose NON Σitj (i, + (i, where N is the number of input views. Following Dong et al. Wang et al. (2025c), the rotation Lg and translation losses L, are calculated
2025
Ci(i. arccos((tr
Rit,) (Re--))RRie.)) 1)/2), (i Ho t(+s).
Here, tr(-) denotes the trace of matrix, and H-(-) calculate the Huber loss.
Opacity Loss. Since a Gaussian is predicted per pixel, the total number of Gaussians grows rapidly the number of views increases. To mitigate this issue, we apply opacity regularization loss following Ziwen et al. (2025) to promote sparsity. Specifically. Lapacity = M EM [o,], where M is the total number of Gaussians. We then prune those with < 0.005. We observed that this removes around 20% 70% of the Gaussians, depending on the number of images and their overlap.
3.5 EVALUATION
For evaluation in the pose-dependent setting, we render the target view using the corresponding ground-truth camera poses. In contrast, under the pose-fre free setting, the predicted camera space may differ from the ground-truth poses obtained via SiM methods. To faithfully assess the quality of Gaussian reconstruction, follow prior pose-free approaches (Ye al. 2025 Wang et al. 2021

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Table I: Novel view synthesis comparison under various input settings. We report results on DL3DV (Ling et al. 2024) with 6, 12, and 24 input views, where p, k. and Opt denote using ground- truth poses, intrinsics, and post-optimization. Our method consistently outperforms previous SOTA approaches, including the pose-dependent DepthSplat, even without prior information.
6v
12v
24v
Method
Opi
PSNR 1
SSIM
LPIPS
PSNR
SSIM 1
LPIPS 5
PSNR 1
SSIM
LPIPS
MVSplat
22.659
0.760
0.173
21.289
0.709
0.224
19.975
0.662
0.269
DepthSplar
23.418
0.797
0.136
21.911
0.753
0.179
20,088
0.691
0.240
Ours
24.717
0.817
0.139
23.285
0,773
0.177
22.664
0.758
0.192
NoPoSplat
22.766
0.743
0.179
19,380
0.563
0.318
17,860
0.495
0.397
Ours
24.887
0.138
23.149
0.758
22.354
0.731
0.205
AnySplat
19.027
0.554
0.235
18,940
0.549
0.262
19.703
0.596
0.249
Ours
24.531
0.804
0.142
22.933
0,746
0.187
22.174
0.720
0.209
IpstantSplat
v
21.677
0.627
0.273
20,792
0.580
0.316
18.493
0,510
0.381
Ours
27.533
0.866
0.106
26.126
0.820
0.133
25.855
0.814
0.136
Fan et al., 2024), which first predict the target camera poses and then render the images using these predicted poses for evaluation. The prediction of target camera poses follows (Ye et al. 20225). which optimizes the poses through a photometric loss based on the predicted 3D Gaussians.
Optional Post-Optimization. After YoNoSplat predicts 3D Gaussians and pose parameters, optionally perform a fast post-optimization. Specifically, we optimize the predicted camera poses along with the Gaussian centers and colors, while keeping all other parameters fixed (see the ap- pendix for details). The results in Tab. show that this optional optimization can further improve performance with reasonable time cost.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We train on RealEstatcI0K (REIOK) (Zhou al. 2018) and DL3DV (Liu et al. 2021) using the official splits. RE10K consists of indoor real-estate videos (67,477 train 7,289 test). DL3DV (Ling et al., (204)) contains 10,000 outdoor videos, 140 for testing. For evaluation on REIOK, we keep test sequences with 2 200 frames (1,580 sequences) and use 6 context views due to the smaller scene scale. On DL3DV, we test with (6, 12, 24) input views and maximum frame gaps evaluate the DL3DV-trained model
(50. 100, 150). For generalization, wanth et al. 2023) by sampling (32, 64, 128) views per sequence with a fixed target view. Inputs are selected by farthest point sampling over camera centers; 8 views randomly held out validation.
ScanNet++ (Yesh-
Implementation Details. YoNoSplat is implemented using PyTorch. The encoder employs the DINOv2 Large model (Oquab al.) 2023) with 24 attention layers, and the decoder consists of 18 alternating-attentic layers. The parameters of the backbone, Gaussian center head, and camera pose head are initialized from T (Wang et al. 2025c), while the remaining layers randomly. During training, we randomly select the number of input views between 2 and 32 views and sample target views. We train models at two different resolutions, 224 x 224 and 280 518. The 224 x 224 model is trained on 16 GH200 GPUs for 150k steps with a batch size of 2 for each, while the 280 x 518 model initialized from the pretrained 224 x 224 weights and further trained on 32 GH200 GPUs for another 1 150k steps with a batch size of 1.
initialized
Evaluation Metries. For the novel view synthesis task, we evaluate with the commonly used met- rics: PSNR, SSIM, and LPIPS. For pose estimation, report the area under the cumulative angular pose error curve (AUC) thresholded at 5 10", and 20 (Sarlin et al. 2020 Edstedt et al. 2024).
Baselines. We compare against SOTA representative sparse- view generalizable methods on novel view synthesis: 1) Optimization-baseed InstantSplat (Fan et al. 2024); 2) Pose-dependent: MVS- plat (Chen al. 2024)) DepthSplat (Xu et al. 2025); 3) Pose-free: NoPoSplat (Ye et al. 2025) and AnySplat (Jiang et al. 2025). For relative pose estimation, we compare against SOTA methods:
MASt3R (Leroy al. 2024)) VGGT (Wang al. 2025a). and 3 (Wang et al. 2025c
4.2 EXPERIMENTAL RESULTS AND ANALYSIS
Novel View Synthesis. To evaluate method complex real-world scenes, test it on DL3DV with varying numbers of input views, scene scales, and input priors. As shown in Table | our model consistently outperforms previous SOTA approaches. Notably, YoNoSplat surpasses leading
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Method
P
PSNR 1
SSIM f
I.PISS 4
SepthSplat NoPoSplat
/
24.156
0.846
0.145
22.175
0.750
0.207
Ours
25.037
0.848
0.134
Ours
25.395
0.857
0.131
Ours
24.571
0.823
0.144
Ours NoPeSplat
DepthSplat
Table NVS comparison on the RE10k dataset (6 input views) under different prior settings. Our model consistently achieves the best performance.
Qualitative comparison on RealEstate (hou et al. 2018). Our pose-free, calibration-ree method enables a more coherent fusion of multi-view contents.
Figure 5:
Groand Truth
Views
Views
128 Views
32 Views
64 Views
Views
SpySp (Trained on ScamNet !)
Ours (no GT Intrinsic, WL.DV-ScoNNe
Figure 6: Qualitative comparison ScanNet++. YoNoSplat generalizes well to ScanNet++ and demonstrates more coherent fusion of Gaussians across different views compared to AnySplat. Moreover, adding more inputs leads to better rendering quality, as more information provided.
pose-free methods like NoPoSplat and AnySplat by a substantial margin. More strikingly, even in the most challenging pose-free, intrinsic-free setting, our model outperforms the SOTA pose- dependent method, DepthSplat, across all view counts. This highlights model's s ability to learn powerful priors that compensate for the lack ground-truth camera information. Qualitatively, Fig. 4 shows that our reconstructions have better cross view consistency, avoiding the artifacts and inaccurate geometry seen in baselines. The results in Table [ also reveal that as the number of input views and scene scale increase (e.g., 12 and 24 views), providing ground-truth extrinsies can further improve performance, acknowledging the inherent difficulty of pose estimation large-scale environments. Furthermore, a fast, optional post-optimization of the predicted Gaussians and poses yields additional performance gains. On the indoor RealEstate dataset (Table (2,, our 6-view model continues this trend, outperforming both pose-free and pose- dependen SOTA methods, as shown qualitatively in Fig. 5
We recommend that readers watch our supplementary videos for more results.
Generalization. To assess generalizability. we train YoNoSplat on the DL3DV dataset and evaluate it on ScanNet++ without fine-tuning. We compare against AnySplat, which is trained on ScanNet++. As shown in Tab, 3 our model significantly outperforms this baseline across all metrics and view counts, despite AnySplat's training-domate advantage. It is worth not- ing that our performance consistently improves as more input views are provided, demonstrating our model's ability to effectively integrate additional information. The qualitative results Fig 6 corroborate this, showing YoNoSplat produces significantly sharper and more coherent reconstruc- tions, demonstrating a better fusion of information across different views. In contrast, the renderings from AnySplat appear blurrier and contain more noticeable artifacts. These findings highlight our model's 's robust ability to generalize to novel datasets not seen during training.
Camera Pose Estimation. As shown in Tab. our model with small resolution input (224 224) already achieves the best performance compared to state-of-the-art methods, while our model with large input resolution (518 x 280) obtains the best performance compared other state-of-the-an approaches. Moreover, we evaluate our model trained on DL3DV but tested on RealEstate 10K (indicated as RRIOK in Tab. 4. ensuring that none of the methods are trained on the RealEstate 10K dataset. The results demonstrate that our method generalizes well and outperforms all baselines, highlighting that training with a rendering loss also benefits pose estimation.
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Table 3: Generalization to ScanNet++, Trained on DL3DV and tested on ScanNet++, our model outperforms AnySplat, despite AnySplat being trained on ScanNet++. Input images are sampled from full sequences with a fixed target view, and our performance improves with more input views.
32v
64v
128v
Method
PSNR 1
SSIM 1
LPIPS
PSNR
SSIM 1
LPIPS
PSNR
SSIM f
LPIPS
SpySpl
14.054
0.494
0.468
15,982
0.551
0.412
16.988
0.583
0.386
Ours w/o GT
16.886
0.600
0,432
17.368
0.608
0.413
17.641
0.617
0.405
Ours w/ GT is
17.935
0.659
0,380
18.833
0.688
0.342
19.284
0.701
0.325
Table Pose estimation comparison. Our method achieves the best pose estimation with smaller
input resolution (224 x 224) and further improves with a larger resolution (518x x 280). We also report zero-shot results on RE10K using a model trained exclusively on DL3DV (so that none of the models are trained on RE1 Ok); our method still outperforms all others.
DL3DV
RealEstate
Method
5 1
10
20"
5
10a f 20"
MASTR 518x289
0.778
0.883
0.941
0.609
0.776
0.878
NoPoSplatzeoxass
0.538
0.735
0.853
0.443
0.627
0.755
VGGT 518x280
0.700
0.848
0.924
0,566
0.753
0.867
7 618x220
0.795
0.897
0.949
0,705
0.841
0.916
224
0.833
0.917
0.958
0.722
0.852
0.923
O2r
(DL3DV-RE10S
0.74
0.859
0.924
OurSc18w290
0.844
0.922
0.961
0,813
0,904
0.951
Ourss18x280
(DL3DV RREOOK)
0.78
0.884
0.939
Table 5: Mix-forcing achieves the best balance Table 6: Pose normalization. Max pairwise dis-
of pose-free free and pose-dependent performance. tance normalization leads to best performance.
Meehod
Pose-uependent
Pose-frce
Norm.
PSNR
SSIM
LPIPS J
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
max. dy
25.212
0.133
Mix-forcing
25.212
0.818
0.133
25.587
0.854
0.130
mean,, dy
24.950
0.845
0.135
Self-forcing
0.815
0.145
max, 22.739
0.756
0.184
Teacher Forcing
25.228
0.850
0.131
25.300
0.851
0.131
No Norm.
22.662
0.757
0.185
4.3 ABLATION STUDIES
For this ablation, we train the model with only 6 imput views for faster training, without compromis- ing generalizability. As a result, the performance is slightly better compared with Tab.
Effectiveness of the Mix-Forcing Strategy. We compare mix-forcing with pure teacher-forcing (ground-truth poses) and self-forcing (predicted poses). As shown in Table self-forcing performs worst in both settings, confirming that entangled pose-geometry learning causes instability. Teacher- forcing excels with ground-truth poses but drops under pose-free evaluation due to exposure bias. Mix-forcing balances these trade-offss, achieving the best pose-free results while remaining compet- itive in the pose- deseedeeeddnnt case, yielding a more robust and versatile model.
Importance of Scene Normalization. As discussed in Sec. 3.3. scene normalization is essential for training on datasets with poses that are only defined up-to-scale. Tab. 6 demonstrates this empiri- cally. Without any normalization, the model's performance severely degraded. We compare our chosen strategy, normalizing by the maximum pairwise distance between camera centers, against two alternatives: normalizing by the mean pairwise distance and by the maximum camera transla- tion from the origin. The results clearly indicate that max pairwise distance normalization yields the best performance. This is because it provides a consistent and robust scale reference for camera translations that aligns directly with the relative pose supervision loss used during training.
CONCLUSION
In this work, we introduced YoNoSplat, a versatile feedforward model for high-quality 3D Gaussian reconstruction from an arbitrary number of images, uniquely capable of operating in both pose- free/pose-dependent and calibraited/uncalibrated settings. We address two key challenges: the en- tanglement of geometry and pose learning, and scale ambiguity. Our novel mix-forcing training strategy resolves the former by balancing training stability and mitigating exposure bias. For the latter, we combine robust max pairwise distance normalization with Intrinsic Condition Em- bedding (ICE) module that enables reconstruction from uncalibrated inputs. These contributions significantly advance the flexibility and robustness of feedforward 3D reconstruction.

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A MORE IMPLEMENTATION DETAILS
Training. During the training process, we first randomly sample a clip from the training videos, the context image are sampled with farthest point sampling based on camera centers from training video clips to ensure sufficient input coverage. The target images whole video clip. We employ the Adam optimizer (Loshchilov & Hutter 2018). setting the initial learning rate for the backbone to x 10-5 and other parameters to 2 x 10 The weight of intrinsic loss Aintrin pose loss Apuse, and opacity loss are set to 0.5, 0.1, and 0.01 respectively. For the mix- forcing training, we set Estert 80k, tend = 100k, and mixing ratio 0.1. Moreover, during the training process, we skip the batches if the pose loss is larger than 1 to avoid abnormal input sequences affect the training stability.
randomly sampled from the
Evaluation. For comparison with baseline methods on novel view synthesis, we use the 224 x 224 version of our model to ensure a fair comparison, it best aligns with the experimental settings of the other baselines. Different prior methods adopt different input resolutions (e.g., MVSplat (Chen al.
2024) and NoPoSplat (Ye et al., 2025) use 256 x 256, while SppthSplat (Xu et al. 2025) uses
256 x 448). Due to computational constraints and to avoid noise from in-house reproduction, is not feasible to retrain all baselines and our model at a unified resolution. However, we have taken care to ensure that the comparisons remain fair and meaningful: 1) Our model has the smallest receptive size among all compared methods. Since all methods first center-crop crop and then resize, square crops result in minimal receptive coverage. We use this model for novel view synthesis comparisons to maintain fairesss 2) Because our model has the smallest receptive size, we can center-crop and resize the rendered outputs of other methods, ensuring that all comparisons are performed on the same image content.
Optional Post-Optiie Dost-Optimization. This fast optimization step refines the predicted camera poses, Gaus- sian centers, and colors for 200 iterations. We use learning rates of 0.005 for pose parameters, 0,0016 for Gaussian means, and 0.0025 for colors. The total optimization time varies with the number of input views: 17.7s for 6 views, 51.1s for 12 views, and 165s for 24 views.
B MORE EXPERIMENTAL ANALYSIS
B.1 ON THE UTILITY OF GROUND- TRUTH POSE PRIORS
A noteworthy and somewhat counter-intuitive result emerges from our experiments, as shown in Tables [ and 2 In settings with a small number of input views (e.g., 6 views), our model operat- ing in the fully pose-free setting outperforms its pose- posendepedd counterpart, which is supplied with ground- -tuth camera poses. We hypothesize that this is due to the inherent noise and potential incon- sistencies within the "ground-truth" poses themselves, which are typically derived from Structure- from-Motion (SfM) pipelines. For sperse-view reconstructions, minor inaccuracies in SfM poses can lead to subtle misalignments when aggregating local Gaussians. In contrast, our pose-frce free model is optimized end-to-end to produce set of camera poses and 3D representation that are maximally photometrically consistent with each other. This internal self-consistency can lead to higher-quality renderings than forcing the model to align with slightly imperfect ground- trutt coordinate system. Moreover, the slight misalignment of the target pose also contributes to this.
However, this trend reverses as the number of input views and the scene scale increase (see Tab. D.. For larger view counts (e.g., 12 and 24), the pose-dependent setting regains its advantage. This occurs because pose estimation becomes more challenging as the scene scale increases, whereas SIM-based ground-trune trutt poses provide strong geometric prior. This analysis highlights the ro- bustness of our model: it can learn priors strong enough to compensate for noisy ground-truth data in sparse-vies view scenarios, while also effectively leveraging ground-truth pose information when the scene scale is large.
B.2 ABLATION ON OUTPUT GAUSSIAN SPACE
As discussed in Sec. 3.1. fundamental design choice is the output representation space. We com- pare our approach of predicting Gaussians a local, vie-view space against the alternative of pre- dicting them directly into unified canonical space. Tab. shows that the local prediction strategy
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Table Comparison of Gaussian representations. Local Gaussian performs better on the 6-view setting.
Representation
PSNR f
SSIM 1
LPIPS
Local Gaussian
25.587
0.854
0.130
Canonical Gaussian
24.104
0.819
0.172
Number Gaustions Views
Memory Views
Prune
AnySplat
20
AnySplat
Number of Gaussinn (X3M)
Ours
Memory (x0 1P-11GB)
15
10
40
100
60
120
Figure 7: Scalability with respect to the number of input views. (a) Average number of Gaussians and (b) memory usage on the ScanNet++ evaluation set when varying the number of input views. "No Prune" denotes vanilla per-pixel Gaussian prediction without any pruning, "AnySplat" Tiang et al. (2025) uses voxel-based pruning, and Ours uses opacity- pruning.
significantly outperforms the canonical one on all metrics. This result empirically validates our hy- pothesis that predicting in local space is more scalable and robust, especially as the number of views increases, as avoids the difficulty foreing a single network align features from multiple views into one arbitrary coordinate frame.
B.3 IMPACT OF INTRINSIC CONDITION EMBEDDING (ICE)
Table Effect of ICE Module. Using the intrinsics predicted by our model leads better perfor- mance compared to training without intrinsic conditioning.
PSNR t
SSIM f
LPIPS
GT Intrinsic
25.587
0.854
0.130
Pred Intrinsie
24.711
0.825
0.141
No Intrinsic
24.481
0.813
0.149
Table 8 evaluates three inference scenarios: (1) ground-truth intrinsics, (2) predicted intrinsics, and (3) intrinsic conditioning. Removing intrinsics causes clear performance drop, confirming their importance for resolving scale ambiguity. Using predicted intrinsics significantly outperforms the no-intrinsic baseline and comes close to ground-truth, demonstrating that ICE enables high-quality reconstruction even from uncalibrated inputs.
B.4 SCALABILITY IN GAUSSIAN NUMBER AND MEMORY
To compare the efficiency our opacity-based pruning with vanilla per-pixel Gaussian prediction and AnySplat, we evaluate all methods on the ScanNet++ dataset while varying the number of in- put views. For each setting, we report the average number of Gaussians and the corresponding memory consumption over all scenes in the evaluation set. As shown in Fig. 7 the "No Prune" baseline exhibits a rapid growth in both Gaussian count and memory as the number of views in- creases. AnySplat mitigates this growth using voxel-based pruning, but still requires large number
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Table 9: Sparse-view novel view synthesis on RealEstate10K using only or input views. Our method matches or surpasses prior approaches with views and achieves clear margin over NoPoSplat when views are available.
Method
PSNR
SSIM1
LPIPS|
PSNRT
SSIMI
LPIPS.
pixelNeRF
19.824
0.626
0.485
pixelSplat
23.848
0.806
0.185
MVSplat
23.977
0.811
0.176
NoPoSplat
25,033
0.838
0.160
26.619
0.872
0.127
Ours
24.917
0.834
0.154
27.528
0.892
0.106
Pose-Free Pose-Dependent
25.39
25.6
25
25.4
25.30
25.29
PSNR
25.2
25.21
25.0
25
55.06
25:00
24.82
0.00
0.05
0,10
0.15
0.20
0.25
0.30
0.35
0.40
Mixing Ratio
Figure 8: Effect of the mixing ratio r. PSNR the RealEstatelOK dataset with input views under pose-free and pose-dependent settings function of the training hyperparameter r. We choose 0.1 as provides good trade-off, improving pose-free performance while keeping pose-dependent performance nearly unchanged.
of Gaussians. In contrast, our opacity- based pruning strategy significantly reduces the number of Gaussians and memory usage across view counts, achieving better scalability than both baselines while preserving reconstruction quality.
B.5 SPARSE-VIEN NOVEL VIEW SYNTHESIS
To assess robustness under extremely sparse observations, we evaluate YoNoSplat on RealEstate 10K using only 2 or 3 input views. As reported in Tab. D our model remains strong in this sparse-view setting. With only views, YoNoSplat attains comparable PSNR/SSIM to the previous state of the art, NoPoSplat, while achieving lower LPIPS (0.154 vs. 0.160), indicating better perceptual quality. When views are available, YoNoSplat clearly outperforms NoPoSplat across all metrics.
B.6 ABLATION ON THE MIX RATIO
We evaluate novel view synthesis performance (PSNR) under both pose-free and pose-dependent settings across different values of the mixing ratio r. Results in Fig. show that larger values of (e.g., 0.2) further improve pose-free performance but substantially degrade pose-dependent performance. We therefore choose r = 0.1, which provides a good trade-off: pose- dese-deeendent perfor- mance remains nearly identical to that of r while pose-free performance improves noticeably. Overall, pose-free free performance consistently increases for moderate values of r (r < 0.3), whereas pose-dependent performance is stable around 0.1 and gradually decreases increases.
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GT
W/O Prune
W/ Prune
GT
WO Prune
Prune
[a]
(b) Specular htr.nularrrranspeeennttt Surfaces
Input
W/O Prune
W/O
Prune
(c) Wide Baselines
Figure 9: Effect of Gaussian pruning under challenging conditions. We compare models without pruning and with pruning applied on (a) thin structures, (b) specular and transparent surfaces, and (c) wide-baseline views. In all cases, pruning does not introduce visible degradation, and the results remain visually consistent with the ground truth.
Table 10: Effect of noise in camera intrinsics novel view synthesis. We perturb the intrinsic parameters by different relative noise levels and report performance with and without intrinsic con- ditioning.
Metrie
No GT Intrinsic
0%
5%
10%
15%
20%
PSNR t
24.711
25.587
24.913
23.681
22.646
21.942
SSIM
0.825
0.854
0.834
0.786
0.742
0.712
LPIPS
0.141
0.130
0.138
0.157
0.179
0.199
B.7 ANALYSIS OF GAUSSIAN PRUNING
We analyze the impact of Gaussian pruning strategy reconstruction quality across challeng- ing visual conditions, including thin structures, specular or transparent surfaces, and wide-baseline viewpoints. These scenarios are known to be sensitive over-pruning, as removing geometrically important but weakly activated Gaussians may potentially degrade fine details or appearance con- sistency. Fig. shows qualitative comparisons between novel views without pruning and those with pruning applied, along with the ground truth. We observe that the rendering quality remains visually indistinguishable after pruning in all tested cases. In particular, fine geometric structures (e.g., lamp stands), reflective or transparent objects (e.g., glassware). and scenes with extremely wide cam- era baselines are preserved without noticeable artifacts or loss details. Quantitatively, we prune Gaussians whose opacity satisfies < 0.005. After pruning, the average PSNR drop over the entire evaluation set is less than 0.01 dB compared to the unprumed model. This confirms that the removed Gaussians contribute negligibly to the final rendering and that our pruning strategy does not sacrifice reconstruction accuracy, even under difficult imaging conditions. Overall, this study demonstrates that Gaussian pruning method is both safe and effective: it significantly reduces the Gaussian number while preserving high-fidelity reconstruction quality.
B.8 EFFECT OF INTRINSIC NOISE ON CONDITIONING
We first investigate how noisy camera intrinsics affect the benefit of our intrinsic conditioning. As shown in Tab. 10 small perturbations (e.g., 5% noise) are handled well: the model with noisy in- trinsics still outperforms the No GT Intrinsic baseline across all metrics, and remains close to the clean-0% setting. When the noise level becomes larger (2 10%), performance naturally degrades, since incorrect physical cues misguide the reconstruction. Nevertheless, these noise levels are sub-
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Views
Views
Views
Views
Views
128 Views
AnySplat
Ours w/o
Our w/p
Ground Truth
Ground Truth
Figure 10: More qualitative comparison on ScanNet+++. YoNoSplat demonstrates strong general- ization to the unseen ScanNet++ dataset, producing more coherent reconstructions than AnySplat by better fusing multi-view Gaussians. The quality improves as more input views are provided (left to right). Notably, while our model performs well without priors (Ours w/o p.), providing ground-truth intrinsics (Ours w/ p.) further enhances generalization and fusion, leading to the highest fidelity results.
stantially higher than those produced by standard calibration pipelines, where intrinsic errors are typically much smaller. Therefore, our conditioning remains robust in realistic scenarios.
C LIMITATIONS
Our method leverages a feedforward approach to reconstruct wide-coverage scenes from an arbitrary number of unposed images. However, the maximum number of input views is constrained by GPU memory. Therefore, an interesting future direction is to explore incremental feedforward reconstruc- tion (Wang et al. 2025b). Moreover, shown in Tab. [ pose optimization can still substantially improve the performance of our Gaussians, indicating that the current feedforward model has signif- icant potential for further enhancement. We also observe that drastic illumination changes between views (e.g., day vs. night) can break photometric consistency. leading to geometric inaccuracies and floating artifacts. Future work could address this issue by explicitly training on datasets with diverse illumination conditions.
D MORE VISUAL COMPARISONS
Here, we provide more qualitative comparisons on the ScanNet++ (Yeshwanth et al. 2023 RealEstate 10K (Zhou et al. 2018), and DL3DV (Ling et al. 2024) datasets. As shown in Fig 10
Fig. II and Fig. 12 our pose-free free method consistently outperforms the previous SOTA pose-free free method, NoPoSplat Ye al. 2025). Moreover,
achieve superior novel view
dering quality compared to SOTA pose-required methods (Chen et al.] 2024, Xu et al. 2025) and optimization-baser methods (Fan al. 2024).
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Published as a conference paper at ICLR 2026
E USE OF LARGE LANGUAGE MODELS
We use LLMs solely for improving grammar, wording, and overall readability of the manuscript. The model is not used for ideation, experimental design, implementation, or analysis. All technical content, methodology, and results are original and developed entirely by the authors.
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Published as a conference paper at ICLR 2026
Input Views
GT
Ours
NoPoSplat
DepthSplat
Figure 11: Qualitative comparison on RealEstate10K Zhou et al. 2018). Our method achieves
high-quality novel view synthesis compared with the previous SOTA pose- pose-required method (Xu et al. 2025) and pose-free method (Ye et al. 2025)
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Published as a conference paper at ICLR 2026
24 View
12 View
EEERE
View
GT
Ours
AnySplat
NoPoSplat
MVSplat
InstantSplat
DepthSplat
Figure 12: Qualitative comparison on DI.3DV (Ling al. 2024). We compare our method with state-of-the-art optimization-based (Fan et al. 2024), pose-required (Chen et al. 2024, Xu et al.) 2025), and pose-free (Ye et al., 2025) Jiang et al.) 2025) methods. Here, the results of our method obtained under the pose-free, intrinsic-free setting. The results demonstrate that our method generates novel view images higher quality than these methods.
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Reviews:
[{"review_id": "US4MwjREZu", "reviewer": "Reviewer_aKcD", "summary": "This paper proposes YoNoSplat, a feed-forward model that predicts per-view local 3D Gaussians together with camera extrinsics and intrinsics from an arbitrary number of unposed and uncalibrated images, and aggregates them into a global scene. The key ideas are:\n\n- Local→Global output space: predict per-view Gaussians plus poses, then transform to a shared frame using predicted or provided poses.\n- Mix-forcing training: a curriculum that starts with teacher-forcing and linearly mixes in self-forcing to mitigate pose-geometry entanglement and exposure bias.\n- Scale ambiguity handling: (i) max pairwise camera-distance normalization of training scenes, and (ii) an Intrinsic Condition Embedding (ICE) module that predicts focal length and conditions the decoder via ray features.\n\nYoNoSplat reports state-of-the-art NVS under both pose-free and pose-dependent settings shows strong cross-dataset generalization to ScanNet++. Optional fast post-optimization of poses/centers/colors offers further gains.", "questions": "1. How sensitive are results to (t_start, t_end, r)? Did you try higher final mixing ratios (e.g., r≥0.3) to further harden the model for pose-free inference? Any signs of instability as r increases?\n2. Normalization choice edge cases. Max pairwise camera-distance normalization is empirically best, but it could be brittle with outliers or heavy view clustering (small parallax + one far-away view). Can this normalization generalizable for those settings?\n3. ICE helps with uncalibrated inputs, but how does performance degrade with systematic intrinsics bias (e.g., ±10–20% focal scale) or radial distortion in images, unseen in training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. **Solid experimental coverage.** Results span multiple priors (p/k/none), multiple view counts (6/12/24; 32/64/128), and include cross-dataset tests (DL3DV→ScanNet++). The pose AUC comparisons further substantiate the quality of the predicted geometry.\n2. **Methodical ablations.** The paper dissects (i) output space (local vs canonical), (ii) training regime (mix/self/teacher), (iii) normalization choices, (iv) ICE usefulness, and (v) Plücker rays, providing good insight into *why* choices matter.\n3. **Clear problem framing & practicality.** Handling arbitrary view counts, unposed + uncalibrated inputs, but also leveraging priors when available, is exactly what real pipelines need. The local-Gaussian design keeps the method compatible with either predicted or GT poses.", "weaknesses": "1. The proposed method predicts per-pixel Gaussians, which may become computationally inefficient for large-scale scenes (with large number of input images). While the paper introduces opacity regularization and Gaussian pruning to mitigate this, it does not quantify how much these steps actually reduce the number of Gaussians or memory footprint. A comparative analysis with AnySplat in terms of Gaussian count and memory efficiency would strengthen the claims about scalability.\n2. ICE train–test mismatch. During training, the network is conditioned on GT intrinsics, whereas at inference it may use predicted intrinsics (when GT is absent). The paper mentions instability when conditioning the decoder on encoder-predicted intrinsics during training but leaves the mechanism under-analyzed. This could introduce residual exposure bias for k. Could further mixing (analogous to mix-forcing for poses) reduce this gap?\n3. Memory limits and pruning are mentioned, but there’s little qualitative analysis on thin structures, specular/transparent surfaces, or very wide baselines (where local Gaussian fusion and pose heads might struggle).\n4. The mix-in schedule seems tuned but not justified beyond a single setting; no sensitivity curve is provided. Could higher r benefit pose-free usage further?", "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": "ht9XgSbe30", "reviewer": "Reviewer_PxrR", "summary": "This paper presents YoNoSplat, a versatile feedforward model for 3D Gaussian Splatting that reconstructs scenes from an arbitrary number of unposed and uncalibrated images. The authors' main contributions are a novel \"mix-forcing\" training strategy to resolve the entanglement between geometry and pose learning and a method to resolve scale ambiguity, enabling the use of uncalibrated inputs. The model achieves state-of-the-art (SOTA) performance in both pose-free and pose-dependent settings, demonstrating significant flexibility and efficiency.", "questions": "The field of 3D reconstruction has recently been revolutionized by unified feed-forward models, such as VGGT and DUSt3R. This progress has spurred the development of conditional input models like Pow3R [1] and MapAnything [2], which can benefit from various optional auxiliary inputs (e.g., camera poses, depth maps, etc.). These methods inherit the advantages of unified feed-forward models and produce a unified representation for a wide range of downstream tasks, including depth estimation, multi-view stereo, camera pose estimation, and novel view synthesis.\n\nIn contrast, the method presented in this paper can only selectively input camera poses and is primarily focused on the novel view synthesis task. What, then, are the specific advantages of this work when compared to these more versatile, multi-task conditional models?\n\n[1] Pow3R: Empowering Unconstrained 3D  Reconstruction with Camera and Scene Priors\n\n[2] MapAnything: Universal Feed-Forward Metric 3D Reconstruction", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is clearly written and easy to follow.\n\n2. The paper's primary strength is the model's versatility. It is designed to handle a wide, practical range of input conditions: an arbitrary number of views, both posed and unposed, and both calibrated and uncalibrated images.\n\n3. The model demonstrates state-of-the-art performance across multiple standard benchmarks.", "weaknesses": "Reliance on Post-Optimization Undermines the \"Feedforward\" Claim. The paper presents itself as a feedforward model, but its strongest results (e.g., in Table 1) rely on an \"Optional Post-Optimization\" step. This optimization is not feedforward and adds a significant time cost (e.g., 165s for 24 views). The large performance gap between the feedforward-only output and the optimized output suggests that the feedforward prediction is, by itself, substantially suboptimal. This weakens the central claim of achieving state-of-the-art results via a purely feedforward pass.", "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": "huPUuR9EGA", "reviewer": "Reviewer_3cs1", "summary": "This paper presents YoNoSplat, a feed-forward model for 3D Gaussian Splatting that predicts per-view 3D Gaussians, camera poses, and intrinsics from an arbitrary number of unposed, uncalibrated images. The paper proposes multiple novel methods for feed-forward 3D Gaussian estimation : (i) local Gaussian estimation continued by a aggregation to the canonical space with the estimated camera pose, (ii) a “mix-forcing” training strategy which first trains the model with ground truth camera poses and progressively blends with the estimated camera poses. Extensive experiments demonstrate state-of-the-art performance on both pose-free and pose-dependent settings, with detailed ablations on normalization and architectural components.", "questions": "Q1. Could the authors provide a comparison with prior works in terms of computation and training time?\n\nQ2. Why is the mix-forcing probability set to an extremely small weight (0.1)? Could the authors provide additional analysis with different weights?\n\nI am eager to raise my score after the authors provide additional explanations for my questions.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- YoNoSplat is able to process images with or without camera pose/intrinsic information, where incorporating additional information of pose/intrinsics leads to improved performance, making it suitable for both in-the-wild settings and industry level deployment.\n- The paper reveals multiple recipes such as scale normalization, mix-forcing, local estimation which is valuable for the research community for future development.\n- YoNoSplat largely outperforms previous approaches, setting the new state-of-the-art for the task.\n- The paper also performs extensive experiments and ablations improving the interpretation and re-implementation of the work easier.", "weaknesses": "- **Large computation and training time:** In Section 4.1, it is explained that YoNoSplat requires 16 GH200 GPUs for 150k steps, where the required computation seems to be extremely large. Adding additional comparison of the required computation with prior works would be nice to better understand the contributions of YoNoSplat.\n- **Architectural Novelty:** The proposed architecture seems to be a direct extension of NoPoSplat with $\\pi^3$, which makes the architectural novelty of the work a bit limited. However, other analyses such as ablations for scene normalization and the intrinsic estimation module seems to be highly valuable.\n- **Overstatement:** The statement in L145-147 : “ In contrast, by replacing point clouds with\n3D Gaussians as the scene representation, YoNoSplat enables both novel view synthesis and training on datasets without ground-truth depth.” seems to be too strong as initializing the weights of YoNoSplat with $\\pi^3$ would be extremely important. I think the statement can be toned down a bit.\n- **Missing baselines:** Teacher forcing has also been explained as one of the important components in a previous work CoPoNeRF [1], where proper reference seems to be missing.", "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": "rNtXq9IAol", "reviewer": "Reviewer_HPFA", "summary": "This paper introduces YoNoSplat, a feedforward framework for 3D Gaussian reconstruction that achieves state-of-the-art performance in both pose-free and pose-dependent settings, across an arbitrary number of input views.\n\nThe authors identify the entanglement between pose estimation and geometry learning as a core challenge in previous 3DGS methods and propose a mix-forcing training strategy that stabilizes optimization and mitigates exposure bias by blending predicted and ground-truth poses during training.\n\nTo further enable reconstruction from uncalibrated images, the paper resolves the scale ambiguity problem via an intrinsic-prediction-and-conditioning pipeline combined with a pairwise distance normalization scheme.\n\nOverall, YoNoSplat offers a single-pass, feedforward solution that unifies pose-free and calibrated reconstruction under one model, achieving efficient, robust, and accurate 3D Gaussian generation from arbitrary view inputs.", "questions": "Could you clarify whether YoNoSplat has been evaluated on single-view or very sparse-view settings, since the paper claims support for “arbitrary numbers of views”?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "(1) Novelty and Conceptual Contribution. As the authors claim, the paper introduces YoNoSplat, the first feedforward framework for 3D Gaussian Splatting (3DGS) capable of operating in both pose-free and pose-dependent settings with an arbitrary number of input views. Unlike prior optimization-based or iterative reconstruction methods, YoNoSplat performs single-pass 3D reconstruction, which is conceptually novel and significantly improves inference efficiency. The identification of pose–geometry entanglement as a key bottleneck and the proposal of a mix-forcing strategy to stabilize training demonstrate deep insight into the learning dynamics of 3DGS systems.\n\n(2) Solid Performance and Robustness. YoNoSplat achieves state-of-the-art performance across multiple benchmarks and scenarios, showing strong results in both calibrated and uncalibrated settings. The method performs consistently well under varying numbers of views, highlighting robust generalization and stability. The new scale ambiguity resolution pipeline and pairwise distance normalization enable accurate 3D reconstruction even from uncalibrated images, a setting where most existing methods struggle.\n\n(3) Thorough Experiments and Ablation Studies. The experimental section is comprehensive and well-designed, covering comparisons with leading 3DGS and feedforward reconstruction models. Ablation studies clearly validate each component — including the effect of mix-forcing, scale normalization, and intrinsic prediction — providing strong empirical justification for the proposed design choices. The experiments are reproducible and transparent, with clear evaluation protocols and diverse test conditions.\n\n(4) Clarity of Writing and Presentation. The paper is clearly written and easy to follow, with a well-organized structure that guides the reader from motivation to methodology and results. Figures effectively illustrate the architecture, pipeline, and visual results, complementing the textual explanations. The tone and presentation are professional and concise, making complex ideas accessible without oversimplification.\n\n(5) Practical Impact and Scalability. By enabling fast, calibration-free 3D reconstruction, YoNoSplat has strong potential for real-world deployment in 3D vision applications such as AR/VR and robotics. Its feedforward nature offers a clear path toward real-time or large-scale reconstruction, marking a meaningful advance beyond optimization-heavy NeRF or 3DGS pipelines.", "weaknesses": "(1) Overclaim on Generality (“Arbitrary-View Pose-Free and Pose-Dependent” Claim Not Fully Convincing). The paper claims that YoNoSplat works for an arbitrary number of views and supports both pose-free and pose-dependent reconstruction. However, the experiments do not fully substantiate this claim. There is no evaluation on single-view or extremely sparse-view scenarios, which are crucial to support the “arbitrary-view” statement. Comparisons focus mostly on internal baselines or a limited subset of view settings. The paper does not include direct comparisons with leading feedforward or hybrid 3DGS methods such as GS-LRM, PixelSplat, VGGT, or DiffusionGS, which already demonstrate strong performance in few-view or uncalibrated settings. As a result, the claimed “state-of-the-art across arbitrary poses and views” feels overstated and not rigorously validated.\n\n(2) Lack of Code and Reproducibility. The code and pre-trained models are not submitted and the reproducibility cannot be checked. Given that the pipeline involves custom intrinsic-prediction modules, mix-forcing training, and pairwise normalization, reproducibility is difficult without access to implementation details. This limitation raises concerns about the verifiability of reported results and practical adoption by the community.\n\n(3) Limited Comparison Scope. The baseline selection is narrow, excluding several state-of-the-art feedforward or diffusion-based reconstruction frameworks (e.g., VGGT, DiffusionGS, PixelSplat). Without quantitative or qualitative comparisons to these strong baselines, it is unclear whether YoNoSplat’s improvements stem from its architecture or from dataset or training differences. The evaluation therefore lacks contextual benchmarking, weakening the impact of its performance claims.\n\n(4) Insufficient Analysis of Failure Cases and Limitations. The paper does not provide a detailed analysis of failure modes, such as how the model performs under extreme camera pose errors, illumination changes, or non-Lambertian scenes. Given that the method aims to handle pose-free inputs, discussing robustness to pose noise would be essential. Similarly, the mix-forcing strategy’s sensitivity to inaccurate supervision or dataset scale is not explored.", "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)