Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
Authors: Yixing Lao
Xuyang BAI
Xiaoyang Wu
Nuoyuan Yan
Zixin Luo
Tian Fang
Jean-Daniel Nahmias
Yanghai Tsin
Shiwei Li
Hengshuang Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: Gaussian Splatting
Texture
Feed-Forward
3D Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decouples geometric complexity from rendering resolution. This approach enables high-fidelity 4K novel view synthesis without per-scene optimization, a capability previously out of reach for feed-forward methods, all while using significantly fewer Gaussian primitives. Project page: https://yxlao.github.io/lgtm/.

Published as a conference paper at ICLR 2026
LESS GAUSSIANS, TEXTURE MORE: 4K FEED-FORWARD TEXTURED SPLATTING
Yixing Lao
Xuyang Bai
Xiaoyang Wu Nuoyuan Yan
Zixin Luo²
Tian Fang
Jean-Daniel Nahmias²
Yanghai Tsin?
Shiwei Li
Hengshuang Zhao
HKU Apple
4K
Feed-Forward Prediction
Compact Geometry
Gaussian
4K
Textures
LGTM
2K
IK
512
256
Existing
LGTM
Flash3D
Flash3D LGTM
SppthSplat
DepthSplat LGTM
Figure I: LGTM enables feed-forward 4K textured Gaussian splatting. LGTM enables high- fidelity 4K novel view synthesis, a capability previously intractable for feed-forward methods. By predicting compact Gaussian primitives paired with per-primitive textures, LGTT overcomes the resolution limitations of prior work, enabling high-quality reconstruction with significantly fewer primitives and no per-scene optimization.
ABSTRACT
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned prim- itives, leading to quadratic growth in primitive count resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such 4K intractable. We introduce LGTM (Less Gaussians, Texture More), feed- forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decou- ples geometric complexity from rendering resolution. This approach enables high- fidelity 4K novel view synthesis without per-scene optimization, capability pre- viously out of reach for feed-ffoward methods, all while using significantly fewer Gaussian primitives. Project page:
githu......tttt........... 1o/1 gtm/
INTRODUCTION
Reconstructing complex scenes and rendering high-fidelity novel views is a key challenge com- puter vision and graphics. Systems addressing this challenge should deliver both efficient feed- forward reconstruction capabilities, allowing the model to instantly reconstruct new scenes without
Work done during an internship at Apple.
Project lead.

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Published as a conference paper at ICLR 2026
requiring additional per-scene optimization, and high-resolution rendering to capture fine details and ensure visual fidelity. These capabilities are crucial for demanding real-world applications, such as augmented and virtual reality, which require both efficient performance and high visual quality to ensure immersive user experiences.
High-resolution feed-forward reconstruction remains challenging. Existing feed-forward 3DGS methods Charatan et al. (2024); Chen et al. (2024a): Fan et al. (2024); Smart et al. (2024); Ye et al. (025 operate resolutions in the hundreds. As Gaussian counts grow quadratically with image size (e.g., scaling from 512 to 4K requires 64x more Gaussians), network prediction and Gaussian rendering become prohibitively expensive high resolutions. Additionally, standard 3DGS cou- ples appearance and geometry within each primitive, requiring an excessive number of Gaussians to represent rich texture regions even on geometrically simple surfaces. While textured Gaussian meth- ods Xu et al. (2024c); Chao et al. (2025), Rong et al.] (2024); [Song et al. (2024); Weiss & Bradley ;2024); Svitov et al. (2025): : Xu al. (2024b) have been proposed reduce primitive counts, they still require per-scene optimization and cannot generalize across scenes in a feed-forward manner.
To address these challenges, we introduce LGTM, a feed-forward network that predicts textured Gaussians for high-resolution novel view synthesis. Our key idea is to decouple the predictions of geometry parameters and per- permmiiitevve textures using a dual-network architecture. LGTM ad- dresses the resolution scalability issue in prior 3DGS feed-forward methods, well the per-scens optimization requirement of existing textured Gaussian techniques. Within the dual-network archi- tecture, primitive network processes low-resolution inputs to predict compact set of geometric primitives, while texture network processes high-resolution inputs to predict detailed per-primitive texture maps. The texture network extracts high-resolution features via image patchification and projective mapping, then fuses them with geometrie features from the primitive network. We adopt staged training strategy: we first pre-train the primitive network to establish robust geometric foundation, and then jointly train it with the texture network to enrich the appearance with high- frequency details. Our framework also versatile, operating with or without known camera poses. In summary, our contributions are as follows:
LGTM the first reed-forward network that predicts textured Gaussians.
LGTM decouples geometry and appearance through dual-network architecture. By predicting a compact set of geometric primitives and rich per-primitive textures, it achieves high-resolution rendering (up to 4K) with significantly fewer primitives than prior feed forward methods.
LGTM is broadly applicable and can be used with various baseline methods. We demonstrate its improvement monocular, two-view and nulti-view methods, with without camera poses.
2 RELATED WORK
Feed-forward 3D reconstruction. Neural Radiance Fields MildeaMildeahall et al. (2020) repre- sents an important advancement in novel view synthesis with neural representations, but its per-scene optimization limits practicality. To address this, generalizable methods Yu et al. 2) Wang et al.) al. [2021); Johari Johari al. (2022) learn cross-scene priors for faster inference. 3D Gaus- sian Splatting (3DGS) Kerbl et al. (2023) enables real-time rendering via explicit primitives but still requires per-scene training, motivating generalizable 3DGS variants Zou al.
(2021); Chen Chen
(2024); 4 Charatan al. et al. (0224) that directly predict Gaussian parameters from posed images. To remove pose dependency, recent works Wang et al. (2024): Leroy et al. (2024) jointly infer poses and point maps, inspiring pose-free Gaussian splatting Fan et (2024): Smart et al. (2024): Ye et al. (2025) and methods that handle more views Wang & Agapito 2025); Tang et al. (2025) ; Wang al. (025b); Zhang et al.
(2024); CCenn et al. (2024a); Wewer et al. (2024); Chen et al. (2024b); Xu et al.
(205)); :025):Wang et (2025a). However, these feed-forward methods predict pixel-aligned point maps or Gaussians resolutions in the hundreds. While images from modern cameras typically 4K
or higher, naively scaling up network resolution results in substantial computational and memory costs, limiting real-world applications.
Textured Gaussian splatting. Traditional 3DGS Kerbl et al. (023) and 2DGS Huang et al. (2024) 3DGS
achieve high-quality view synthesis by optimizing Gaussian primitives, but their coupling of ap- pearance and geometry is limiting. Since each Gaussian encodes only a single (view-dependent) color, representing high-frequency textures complex reflectance demands excessive number
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Published as a conference paper at ICLR 2026
Rendering Resolution
Primitive Resolution
Texture Size
Metries
Train Memory
Method
LPIPS
SSIM
PSNR
(bs 1)
NoPoSplat
x026
0.239
0.716
23.169
61.85 GB
1724x576
LGTM
512x288
2x2
0.213
0.816
25.606
20.16 GB
LGTM
256x 144
4x4
0.283
0.762
24.135
16.26 GB
NoPaSplat x058x1152
OOM
2048x1153
L.TMM 512x288
4x
0.176
0,810
25.328
21.39
LGTM
256x 144
8x8
0.246
0.759
23.628
17.46 GB
NoPaSplat
4096x2304
OOM
4096x2304
L.TTM 512x288
8
0.200
0.803
24.489
28.23 GB
Table I: LGTM enables high-resolution foed-forward Gaussian splatting. We compare LGTM with NoPoSplat Ye et al. (2025) variants using different primitive resolutions but yielding the same effective output resolution. NoPoSplat fails to train 2K or higher due memory and compute limits, whereas LGTM reaches equivalent resolutions with compact geometric primitives and per- primitive texture maps. Training memory reports peak GPU usage with batch size 2 context views, and 4 target views. For inference performance and memory analysis, see Table
of Gaussians, even for simple geometry (e.g., a flat textured surface). To improve the efficiency of appearance representation, recent works explore integrating texture representations. One strategy involves using global UV texture atlases shared by all Gaussian primitives Xu et al. (2024c). How- ever, optimizing such global texture maps can be challenging for scenes with complex geometric topologies. A more flexible approach employs per-primitive texturing by assigning individual tex- tures to each Gaussian. This includes 3DGS-basee approaches Chao et al. (2025); Held et al. (2025)
and 2DGS-based ones Rong et al. (2024); Song et al. (2024); Weiss & Bradley (2024); :); [Svitov et al.
2025); Xu et al. (0024b). The type of texture information used in these per-primitive methods varies. Some methods employ standard RGB textures Rong al. (2024); (ong al. (2024) ; Weiss & Bradley (2024), some introduce additional opacity maps Chao et al. (2025); Svitov et al. (0255)
while others utilize spatially-warying functions for color and opacity instead Xu al. (2024b) Held
et al. (2025). While these textured approaches effectively decouple appearance and geometry for high-fidelity rendering, they require per-scene optimization, meaning separate optimization pro- cess must be performed for each new scene.
3 PILOT STUDY
We conducted pilot study validate the motivation behind our work: addressing the resolution scalability bottleneck of forw-fo methods. As shown Table when scaling NoPoSplat Ye et al. (2025) to output 1024x576 primitives, training memory already reaches 61.85 GB for a batch size of I. Moreover, training fails entirely at 2K and 4K resolutions due to memory constraints.
LGTM trains successfully 2K and 4K using under 30 GB of memory (Table TT and scales effi ciently at inference: a 64x pixel increase adds only modest memory and time overhead (Table 4 This enabled by decoupling geometry from appearance LGTM maintains compact set of geometric primitives and scales per-primitive textures to reach higher resolutions. This approach exploits the natural frequency separation scenes: low-frequency geometry vs. high-frequency appearance. Moreover, LGTM offers tunable trade-off between primitive size and texture size.
4 METHOD
LGTM provides general framework that be applied to multiple bascline methods with dif- ferent input settings, such as monocular (Flash3D Szymanowicz et al. (2025), posed two-view (DepthSplat Xu al. (0244)), unposed two-view (NoPoSplat Ye al. (2025)), and multi-view (VGGT Wang et al. (2025a)) inputs. The LGTM feed-forward network predicts a set of textured 2D Gaussians from set of input images [In] J and its low-resolution counterparts 1.
f (I" ] (1i 86, Pi, Cr, TÇ, T.).
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312
VIT Encoder
VIT Decoder
prze
Primitive Head
4096 2504
p.s.r.c
4096
2004
F pti
Image Patchify
Feature Aggrogator
Piss
Texture Head
r'T" Rad
Textured Gaussians
Novel View Rendering
Projective
Mapping
Sprre
Suecaue
Inputs Views
Figure 2: LGTM Architecture. Top: The primitive network Fprim takes low-resolution images as input and predicts compact geometric primitives 8, r, C. Bottom: The texture network frextuee processes high-resolution images through image patchify and projective mapping networks, and predicts per per-primitive texture maps T T". This decoupling of geometry and appearance enables LGTM achieve feed-forward 4K Gaussian splatting with significantly fewer primitives.
The network is composed of two main submodules: a primitive network, .prim that predicts compact 2DGS geometric primitives, and texture network, fiextue. that predicts rich texture details. We first introduce the preliminaries of 2DGS and textured Gaussian splatting, and then present the details of our LGTM framework.
4.1
PRELIMINARIES
2D Gaussian Splatting. 2D Gaussian Splatting (2DGS) Huang al. (2024) represents scenes with a set 2D Gaussian primitives in 3D space. To render an image from view v, for each pixel coordinate (x, y). we find its local coordinates the ray-splat intersection: the intersection point of the camera ray passing through x with primitive This process is encapsulated by 2D homography transformation M, where Mı, The local coordinate u is then used to evaluate 2D Gaussian function (zu) exp( 1 (u² + +²)). The
(u, v) primitive's 's plane by computing
alpha value a, and color è for this sample are given by:
a, (u) (d4)) = SH(c,, d:),
(()),
where d, is the view direction. The final pixel color C' is computed by ddpha-blending the contribu- tions from all primitives sorted by depth: C Σ; E. (d.)) (1-a)- (u,)).
Textured Gaussian Splatting. Inspired by classic billboard techniques Décoret et al. (2003) and following the recent BBSplat work [Svitov al. (025)), we augment the standard 2DGS primitive with learnable, per-primitive texture maps: color texture TÇ E IR. Tx and alpha texture To RTXT, where T is the texture resolution. At ray-splat intersection point u, we retrieve color and alpha values from these maps using bilinear sampling (Sec.[A.2) denoted by the bracket notation T(u]. The alpha texture replaces the Gaussian falloff, and the color texture adds detail to the SH base color. The sample's alpha and color are thus redefined as:
(x(u)= = T [u], (u, [u] + SH(C, d.).
4.2 FEED-PORWARD PREDICTION OF TEXTURED GAUSSIANS
LGTM employs dual-network architecture that decouples geometry and appearance prediction, as illustrated Fig. 2 The primitive network fprm takes low-resolution images as input and predicts compact geometric primitives μc, images through image patchify and projective mapping networks, and predicts per-primitive texture maps T T To stabilize the training, adopt staged training recipe: first establish robust geometric foundation, then introduce textural details.
The texture network fiexure processes high-resolution
2DGS pre-training high resolution. In the first stage, train the primitive network fprm to predict 2DGS parameters:
Sprim
(Frim Hi, Si, Pi; Ois Cz),
(3)
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Published as a conference paper at ICLR 2026
where prim are the feature maps from the ViT decoder that will also be shared with fiexure- The primitive network takes low-resolution version of the input, fow E Rxxuxs, and processes through a VIT encoder-decoder to predict the scene's geometry and low-frequency appearance, pro- ducing a grid of h x w 2DGS primitives.
The key idea is high-resolution supervision: the network takes low-resolution inputs Low and pre- dicts an h x w primitive grid, but renders and supervises them at full resolution H x W. Although the predicted Gaussians can be rendered at arbitrary resolutions, doing so without high-res supervision may result in areas with holes, as they are not anti-aliased (see supplementary Sec. A. for details). The high-resolution supervision forces the network to learn predictive scales s and other parameters appropriately sized for high-resolution rendering, establishing a strong geometric prior.
Learned projective texturing. The texture network fiexture takes in high-resolution images well as low-resolution primitive features prim and predicts per-primitive texture maps T T
fiexure : (T", Fpim) - (T. , ).
(4)
At a high level, fiexture combines three complementary features: F patch is computed from the patchi- fied high-resolution image followed by convolutional layers to encode local features; the projective features Frojj are extracted from projective prior textures, which provide strong high-frequency tex- ture details; and Fpim reuses backbone features. These features (Fr to predict the final per-primitive textures (
F proj) are aggregated
To compute projective features F proj. we perform projective texture mapping from the image back to the textured Gaussian primitives. For each Gaussian primitive i, we compute projective prior texture T using the inverse transformation M u + that maps from primitive local coordinates u to source image pixel coordinates æ, and then sample the RGB color from the high- resolution source image I" x::
Te [u] [" [MM
(5)
Intuitively, the projective prior T.ep [u] is computed by the inverse process of Gaussian rasteri- zation: instead of rendering Gaussians to an image, we "render" the source image back onto the Gaussian texture maps using the inverse transformation. This projection step is highly efficient it can typically be done in a few milliseconds for a 4K image. Finally, we extract projective features (r]] to provide strong high-frequency appearance features for texture prediction.
Fy from proj
Training recipe. LGTM employs a progressive two-stage training strategy that gradually intro- duces texture complexity while maintaining geometric stability. In the first stage, we train the primitive network fprim in isolation to predict 2DGS parameters using low-resolution inputs with high-resolution supervision. In the second stage, we jointly train both the primitive network fprim and texture network fiexure. To maintain geometric stability, the pre-trained primitive network pa- rameters are trained with a reduced learning rate (0.1x). The color texture T is zero-initialized and added to the SH base color (Eq. 2 to provide high- high-frequency color details. Both stages are supervised with standard photometric losses (MSE LPIPS).
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Baselines. LGTM can be applied to most existing feed-forward Gaussian splatting methods to enable high-resolution novel view synthesis. We evaluate LGTM across three scenarios with the following baseline methods: single-view with Flash3D Szymanowiez et al. (2025), two-view with both the pose-free NoPoSplat Ye et al. (025) and the posed DepthSplat Xu et al. (2024a), and multi- view with Wang et (2025a). For each scenario, we compare three variants: 3DGS, 2DGS, and LGTM. For the 3DGS and 2DGS baselines, we re-train them with high-resolution supervision (Sec. T. We train and evaluate across multiple resolutions and report standard image quality metries: LPIPS SSIM1, and PSNR*.
Datasets. We evaluate LGTM with RealEstate (RE10K) Zhou et al. (0018) and DL3DV-
10K [ing et al. (2024). For RE10K, follow the official train-test split consistent with prior
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Mcthod
Render Resolution
Primitive
Texture Size
Metrics
Dataset
Baseline
Primitive
Resolution
LPIPS
SSIM
PSNRT
3DGS
1024x576 (IK)
512x288
0.250
0.823
24.666
NoPoSplat
2DGS
1024x576 (1K)
512x288
0.225
0.829
24.674
LGTM
1024x576 (1K)
512x288
2x2
0.195
0.847
25.419
REIOK
3DGS
2048x 1152 (2K)
512x288
0.257
0.819
24.235
NoPoSplat
2DGS
2048x1152 (2K)
512x288
0.227
0.823
24.282
LGTM
2048 1152 (2K)
512x288
4x4
0.182
0.856
25,233
3DGS
1724x576 (IK)
512x288
0.297
0.747
24.131
NoPoSplat
2DGS
1024x576 (1K)
512x288
0.260
0.736
23.569
LGTM
1024x576 (1K)
512x288
2x2
0.213
0.816
25.606
3DGS
2048 1152 (2K)
512x288
0.292
0.737
23.814
NoPoSplat
2DGS
2048x1152 (2K)
512x288
0.262
0.731
23,427
LGTM
2048x (2K)
512x288
4x4
0.176
0.810
25.328
DL3DV
3DGS
x920x 1024 (2K)
960x512
0.161
0.826
25.705
DepthSplat
2DGS
1920x 1024 (2K)
960x512
0.164
0.823
25.967
LGTM
1920x 1024 (2K)
960x512
2x2
0.159
0.832
26.218
3DGS
2006 (4K)
512x288
0.351
0.753
23.022
NoPoSplat
2DGS
4096xx304 (4K)
512x288
0.322
0.737
22.198
LGTM
4096x2304 (4K)
512x288
8x8
0.200
0.803
24.489
3DGS
2040 (4K)
960x512
0.210
0,801
24.740
DepthSplat
2DGS
3840x2048 (4K)
960x512
0.198
0.794
24.715
LGTM
3840x2048 (4K)
960x512
4x4
0.170
0.827
25.508
Table 2: Novel view synthesis results under two-view setting. Performance of LGTM over NoPoSplat and DepthSplat across rendering resolutions and datasets. For 4K resolution, we use for NoPoSplat and 3840x2048 for DepthSplat due the requirements of their back-
bone networks.
work Charatan CharaCaa et al. 2024), and report results up to 2K resolution For DL3DV, we use the bench- mark subset for testing and the remaining data for training, and report results up 4K resolution to demonstrate high-resolution feed-forward reconstruction and novel view synthesis.
5.2
MAIN RESULTS
Two-view. Table 2 presents our main results for two-view novel view synthesis on the REIOK and DL3DV datasets. We evaluate LGTM with two baselines: pose-free NoPoSplat and posed DepthSplat. For both baselines, LGTM consistently outperforms the 3DGS and 2DGS variants across all tested resolutions and all metrics. A similar trend is observed on the higher-resolution DL3DV dataset, where L.GTM again consistently surpasses baseline performance 4K. Beyond improvements on pixel-wise metrics PSNR and SSIM, LGTM shows stronger improvement on the perceptual metric LPIPS with a 23% 75% reduction. Fig. 3 shows novel views synthesized by LGTM and the baseline methods on the DL3DV dataset at 4K resolution. The comparison between baselines and LGTM indicates that LGTM general and effective for modeling gigh-freueecy details with compact texture maps, leading higher-fidelit renderings.
Single-view. Table 3 shows results for single-view novel view synthesis on the DL3DV dataset. With Flash3D Szymanowicz et al, (2025) the baseline, LGTM again achieves the best perfor- mance on all metrics at all resolutions. Fig. provides a qualitative comparison showing that LGTM renders finer details and textures, which are often blurred or lost by baseline methods due to their limited number of geometric primitives. Notably, LGTM achieves high-quality renderings with only 512x288 geometric primitives, strongly demonstrating the power of rich per-primitive textures.
'The terms "2K" and "4K" refer to horizontal resolutions of approximately 2,000 and 4,000 pixels. respec- tively. RE10K offers 2K x920x 1080 resolution (also commonly known 1080p) for a subset of its video sources, while D1-3DV offers 4K resolutions x

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NoPoSplat
SppthSplat
NoPoSplat LGTM DepthSplat + LGTT
Figure 3: Qualitative comparison in the two-view setting. Evaluated the DL3DV dataset 4K resolution. Best viewed in color and zoomed in.
Method
Render Resolution
Primitive Resolution
Texture Size
Metries
Inputs
Baseline
Primitive
LPIPS
SSIM
PSNR
3DGS
1024x576 (1K)
512x288
0.274
0.672
21.353
Flash3D
2DGS
1024x576 (1K)
512x288
0.267
0.676
21,447
LGTM
1024x576 (1K)
512x288
2x2
0.215
0.708
21.910
3DGS
2048x (2K)
582
0.368
0.643
19.911
Single-view
Flash3D
2DGS
2058x1152 (2K)
512x
0.358
0.653
20.276
LGTM
2048x1152 (2K)
512x
4x4
0,224
0.712
21.552
3DGS
4096xx304
(4K)
512x288
0.399
0.724
20.068
Flash3D
2DGS
4096xx304 (4K)
512x288
0.371
0.725
20.322
LGTM
4096x2304 (4K)
512x288
8x8
0.219
0.766
21.778
3DGS
1036x560 (1K)
518 288
0,336
0.649
20.278
VGGT
2DGS
1036x560 (1K)
518x280
0.341
0.646
20.177
LGTM
1036x (1K)
518x280
2x2
0.325
0,676
20.645
Multi-view
3DGS
2072 1120 (2K)
518x280
0.380
0.644
19.461
VGGT
2DGS
2072 1120 (2K)
518
0.392
0.641
19.273
LGTM
2072x 1120 (2K)
518x280
4x4
0.361
0.661
19.990
Table 3: Single-view and multi-view novel view synthesis results. Comparison of LGTM with Flash3D) [Szymanowiez et al. (2025) and VGGT Wang et al. (2025a) baselines across different view settings and resolutions on DL.DV dataset.
Multi-view. To demonstrate that LGTM is a general framework supporting different numbers of views as input, we build a 4-view feed-forward LGTM variant with VGGT Wang al. (2025a). We implement a Gaussian prediction head over the VGGT backbone following AnySplat Tiang et al. (025) to poses to mitigate potential misalignment between rendered novel views and ground truth. As shown in Table LGTT achieves consistent improvements 1K and 2K resolutions. We did not scale up
baseline. During training, align predicted camera poses with ground truth

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Flash3D + 3DGS
Flash3D + 2DGS
Flash3D + LGTM
GT
Figure 4: Qualitative comparison in the single-view setting. We re-train Flash3D on DL3DV with the same number of geometric primitives (512x288) for 3DGS, 2DGS, and LGTM.
to 4K resolution due to memory constraints even with the VGGT backbone frozen, which we leave for future work.
Performance benchmark. Table presents detailed inference performance analysis for the two-view synthesis task, where two views are input to predict single target view. We benchmark on single NVIDIA A100 GPU using a batch size one; for each case, we report peak memory and average timings over 10 runs after 3 warmups. LGTM is highly efficient when scaling to high resolutions. This highlighted by comparing the NoPoSplat 512x 288 2DGS model (@) with the LGTM 4096x2304 model (W): for 64x increase in pixels. LGTM requires only 1.80x the peak memory and 1.47 the total time. The scalability advantage of IGTT''s texture-based upsampling becomes increasingly pronounced higher resolutions, where traditional methods face prohibitive costs. While Thows inference performance, Table Janalyzes training memory requirements.
Primitive Resolution
Texture
Render Resolution
Peak GPU
Network Fwd. Time (ms)
Render Time (ms)
Total Time (ms)
Method
Size
Memory (GB)
NoPoSplat 3DGS
512x 512x:288
512x288
3.06
110.37
3.81
1 114.18
NoPoSplat 2DGS
512x
3.06
112.70
6.43
119.13
LGTM
512x
2x2
x724
4.33
132,06
7.70
139.76
LGTM
512x288
4x4
2158x1152
4.60
136.14
14.26
150.40
LGTM
512x288
8x8
4096xx304
5.51
142.28
32.82
175.10
Table 4: Inference time and memory benchmark. LGTM is highly efficient when scaling up to high resolutions: compared to the NoPoSplat 512x288 2DGS (2), the LGTM 4096x model
(5) represents 64x increase in pixels, yet requires only 1.80x the peak memory and 1.47x the total time. For an analysis of training memory requirements, see Table
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Method
LPIPS
SSIMT
PSNR
NoPoSplat (3DGS)
0.371
0.583
16.964
High-res retrain (2DGS)
0.256
0.731
23.502
Image patchify only
0.199
0.782
24.673
+
Texture color
0.189
0.806
25.314
+ Texture alpha (full model)
0.176
0.810
25.328
Table 5: Ablation study on LGTM components. We start from the NoPoSplat baseline and pro- gressively integrate components of LGTM, evaluated DL3DV at 2K resolution. All models operate on x88 primitives. The baseline is trained on low-resolution and evaluated at 2K. Sub- sequent versions trained with high-resolution supervision.
BRE
TRE
Baseline i
+ High-re retrain @
Image patchify only @
Full model @
Qualitative results of the ablation study. Each image shows the progressive improve- ment components of LGTM are added to the NoPoSplat baseline. The full model produces renderings that are visibly closer to the ground truth.
Figure 5:
5.3 ABLATION STUDY
We conduct an ablation study to analyze the contribution of each component of LGTM, with results presented in Table Our base model (1) is NoPoSplat Ye al. (2025) using 3D Gaussians, trained on low-resolution inputs and evaluated at 2K, which yields poor performance. Simply applying high-resolution supervision (2) significantly improves results, establishing a stronger baseline for comparison. From this improved baseline, we introduce the
components of LGTM. First, adding image patchified features (3) provides a substantial boost across all metrics, demonstrating effectiveness in capturing high-frequency details. We then add a learned texture color map (@), which further improves performance by enriching the appearance details. Finally, incorporating learned texture alpha map in the full LGTT model (@) yields the best results, confirming that both texture color and alpha are essential for high-quality rendering. The qualitative comparison in Fig, visually reinforces these findings, showing clear progression in rendering quality.
CONCLUSION
We introduce LGTM, a feed-forward network that predicts textured Gaussians for high-resolution rendering. LGTM addresses the resolution scalability barrier that has limited feed-forward 3DGS methods to low resolutions. LGTM achieves 4K novel view synthesis where traditional approaches fail due to memory constraints, requiring only 1.80x memory and 1.47 x time for 64x increase in pixel count. The consistent improvements across multiple baseline methods (Flash3D, NoPoSplat, DepthSplat, VGGT) demonstrate the broad applicability of our approach.

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Published as a conference paper at ICLR 2026
Limitations. While LGTM addresses texture scaling well, reconstruction quality still depends heavily on geometry. Empirically, LGTM performs best in the single-view setting (Flash3D) with- out multi-view inconsistency, better in the posed two-view setting (DepthSplat) than the unposed one (NoPoSplat) due to improved geometry, and shows marginal gains in the multi-view setting (VGGT) where geometry is less precise. Additionally, our current framework operates with pre-defined tex- ture resolutions, requiring manual tuning of texture size to balance quality and computational cost.
This work is supported by the Hong Kong Research Grant Council General Research Fund (No. 17213925) and National Natural Science Foundation of China (No. 62422606).
Acknowledgments.
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LESS GAUSSIANS, TEXTURE MORE: 4K FEED-FORWARD TEXTURED SPLATTING
SUPPLEMENTARY MATERIALS
TABLE OF CONTENTS
A.1 High-Resolution Re-training
13
A.2 Bilinear Texture Sampling
13
.3 Robustness to Larger Input View Gaps
14
A.4 Comparison with Per-Scene Optimization
15
A.5 Use of Large Language Models
16
A.1 HIGH-RESOLUTTIO RE-TRAINING
Baseline methods such NoPoSplat and DepthSplat are typically trained at low resolution (up to 1K). When building high-resolution LGTM variant from the baseline, we first re-train the base- line with high-resolution supervision. This is crucial for the network to learn primitive scales and other attributes properly for high-resolution rendering, serving as a strong geometry prior for texture learning. Here demonstrate the effectiveness of high-resolution re-training by comparing three methods: Direct Render directly renders the Gaussians produced by low-resolution baseline models the target resolution; Render and Upsample renders the output Gaussians training resolution, then upsamples the resulting image to the target resolution; High- Righ-Resolutton Re-train re-trains the baseline model by rendering and supervising at the target resolution.
Bent
Direct Render
Render and Upsample
4K Re-trained Baseline
Figure 6: Effects of high-resolution re-training.
As shown in Fig. 6. direct rendering of Gaussians at higher resolution produces holes because 3DGS lacks anti-aliasing. Rendering and upsampling eliminates holes but produces blurry results since the effective image resolution remains low. Re-truin produces the best results among the three methods, demonstrating the effectiveness and necessity of high-resolution re-training. This strong geometry prior then serves as the foundation for LGTM to learn textural details. Re-train produces slightly better results than Render and Upsample still suffers from blurriness due the limited texture complexity of vanilla 3DGS. In Table we use the render and upsample strategy to evaluate baseline methods.
A.2 BILINEAR TEXTURE SAMPLING
Given a texture map of resolution T x T and a ray-splat intersection point with local coordinates (u, v) the primitive plane, retrieve the texture value via bilinear sampling with border clamp- ing, as detailed in Algorithm|] I The scale a shared hyperparameter that controls the span of the texture region within the 2DGS primitive.
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Published as a conference paper at ICLR 2026
Algorithm Bilinear texture sampling at local coordinates (u, v)
1: function BILINEAR-SAMPLEC 0,)
2: (ata) T 0.5
Transform to pixel coordinates
2g 3: [uta] T - 0.5
4: u - clamp(u, (1 0,
D Border clamping
5:
clamp(u, 0, - 1)
6:
du < [u], fu f u in
b Integer and fractional parts
7:
tu [. fo UU-- io
8:
but1 < min(l, + 1.T 1), 1011 + min(2g + +1. 1,T--1
> Corner indices
9:
return (1- fa) fu) The dol + fu(1 fu) 2
10:
+(1 -11-f)) fa)f Tiu bot1] + sulv [uut1,
end function
-1.-
05-
13
RGB Texture (4x4)
Unbounded Sampling
Figure 7: Unbounded bilinear texture sampling. Our sampling method uses border clamping handle out-of-bounds coordinates, allowing texture information extend beyond the primitive boundary. This provides smoother transitions and better coverage compared to bounded sampling approaches that use zero-padding for out-of-bounds areas.
For texture colors, we use an unbounded bilinear sampling strategy that differs from BBSplat Svi- tov et al. (2025). In BBSplat, color sampling is bounded and the color decays from the Gaus- sian center, while traditional non-textured 2DGS maintains constant colors with only opacity decay. Our unbounded sampling ensures equal weights across the four bilinear corners, making it closer to the original non-textured 2DGS formulation and allowing us to learn texture colors using only 2DGS opacity as alpha fall-off. This prevents dark edge artifacts that would result from combin- ing BBSplat's bounded sampling with regular 2DGS opacity. Our method is similar to Gaussian Billboards Weiss Bradley (2024), where a controls the relative span of the textured region.
A.3 ROBUSTNESS TO LARGER INPUT VIEW GAPS
To evaluate whether LGTM's textured Gaussian representation works well beyond small viewpoint differences, we assess novel view synthesis quality under different camera pose settings with grad- ually increasing context view differences. We vary the context view gap on the DL3DV test set to examine how performance changes as the baseline between input views increases.
For each context frame index gap {10, 20, 30, 40), we select 2 source views with the specified gap and sample 4 target views evenly distributed within the range for numerical evaluation. Both No- PoSplat baseline and NoPoSplat + LGTT use 512x288 Gaussians per-view with 2 context views, trained and evaluated 4096 x006 resolution, with the baseline trained with high-resolution su- pervision (Sec. A.I).
Fig. 8 provides qualitative comparisons across different frame gaps. Each row shows the two context views along with target view renderings from both NoPoSplat baseline and NoPoSplat LGTM. As the gap increases from 10 40 frames, the viewpoint differences become more substantial, mak- ing the reconstruction task increasingly challenging. Despite this, NoPoSplat + LGTM consistently produces sharper details and fewer artifacts compared to the baseline across all settings, confirming
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the quantitative results. Table shows that NoPoSplat + LGTM consistently outperforms the base- line across all context view gaps. While both methods experience performance degradation as the gap increases, NoPoSplat LGTM maintains its superiority difference substantial. These results demonstrate that per-primitive texture maps handle novel viewpoints beyond small differences, addressing concerns that the method might only work well for closely spaced input views.
at gap 40, where the viewpoint
BAN
Context #1
Context #2
NoPoSplat
NoPoSplat LGTM
Figure 8: Qualitative comparison across different input view gaps. Each row shows context frame gaps of 10, 20, 30, and 40 frames. LGTM consistently produces sharper details and fewer artifacts.
Context view gap
Method
LPIPS
SSIMI
PSNR1
NoPoSplat
0.322
0.737
22.198
NoPoSplat + LGTM
0.200
0.803
24.489
NoPoSplat
0.366
0.722
20.732
NoPoSplat LGTM
0.258
0.765
22.159
NoPoSplat
0.413
0.709
19,430
NoPoSplat + LGTM
0.323
0.730
20.123
NoPoSplat NoPoSplat + LGTM
0.458
0.696
18.264
40
0.376
0.707
18.862
Table 6: Quantitative comparison across different input view gaps. LGTM consistently out- performs the baseline across all gaps (10, 20, 30, 40 frames), demonstrating robustness to larger viewpoint differences.
A.4 COMPARISON WITH PER-SCENE OPTIMIZATION
We compare DepthSplat + LGTM with per-scene optimized 3D Gaussian Splatting on DL3DV at 4K. Both methods use context views (frames and 20) and are evaluated on 19 target views (frames 1-19). For per-scene optimization, we run COLMAP structure-from-mmolee
on all frames (0, 1, .... 20}. as running COLMAP on only 2 context frames fails. We then filter the sparse point cloud to retain only points visible from the context views for initialization. We optimize using the 2 context views at 4K following the default 3DGS protocol, with 30,000 iterations, refinement every 100 iterations, and densification stopping 15,000 iterations.
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Published as a conference paper at ICLR 2026
Table that DepthSplat LGTM outperforms per-scene optimization across all metrics while being orders of magnitude faster. The optimization-bn basioo approach overfits to the context views, as shown in Fig. 9 where its PSNR drops ~20-22 dB for middle frames while DepthSplat LGTM maintains stable performance. By training on diverse scenes, DepthSplat + LGTT develops strong novel viewpoints, whereas per-scene optimization only interpolate
priors for generalizing between limited training views. DepthSplat + LGTM achieves instant reconstruction compared to 30 minutes for per-scene optimization on single A100 GPU.
PSNR (dB)
24
LGTM
Optimization
2
10
11
12
13
14
15
16
17
19
Target Framne Index
with cnmiimminnta
Per-Scene Optimization
DepthSplat LGTM (Ours)
Figure 9: Comparing LGTM to per-scene optimization. Top: Per-scene optimization overfits to context views with degraded performance on middle frames. DepthSplat + LGTT maintains stable quality across all frames. Bottom: Target frame #10. While both achieve similar sharpness in the center, LGTM produces sharper details with fewer artifacts toward the edges.
Method
Per Per-Scene Optimization DepthSplat LGTM (Ours)
Frames for COLMAP
{0. 1, 2, .... 20}
Context views (optimization)
{0, 20)
{0, 20}
Target views (evaluation)
2, .... 19}
(1.2.3..... 19)
PSNR
21.75
27.99
SSIMI
0.78
0.88
LPIPS
0.21
0.15
Table 7: Comparison with per-scene optimization. DepthSplat LGTM outperforms per-scene optimized 3DGS while being orders of magnitude faster.
A.5 USE OF LARGE LANGUAGE MODELS
Declaration of use of Large Language Models (LLMs): We use LLMs to help us polish sentences in the manuscript and fix grammar and spelling mistakes.
16


Reviews:
[{"review_id": "w3lb9Y93o0", "reviewer": "Reviewer_wL1e", "summary": "This work proposes LGTM, a feed-forward framework that predicts compact Gaussian primitives coupled with per-primitive textures for high-resolution novel view synthesis. By decoupling geometry and appearance with a dual-network design, the method enables 4K rendering with significantly fewer primitives and without per-scene optimization. LGTM is applicable across various feed-forward 3DGS baselines, including single-view, two-view, and multi-view setups, and demonstrates consistent quantitative and qualitative improvements on several benchmarks.", "questions": "Kindly refer to [Weaknesses] section.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "* The idea of combining low-resolution Gaussian primitives with high-resolution texture maps to achieve high-resolution feed-forward Gaussian Splatting is well-motivated and sound. Such an insightful finding may significantly boost the feed-forward 3DGS community to explore higher-quality synthesis.\n\n* The introduced module is architecture-agnostic and is thoroughly evaluated across several state-of-the-art models and large-scale benchmarks. All experiments show consistent quantitative and qualitative improvements.\n\n* The manuscript is well structured and easy to follow.", "weaknesses": "* Lack of multi-view results. It would be better to provide video results or multi-view images to better illustrate the impact of the texture modules. I am concerned that the texture module may potentially destroy multi-view consistency to some extent.\n\n\n* Lack of evaluation under dense multi-view settings. Most experiments are conducted with 1, 2, or 4 views. Since the multi-view model is based on VGGT, which natively supports dense input views, it would be better to include results with denser settings, such as 32 or 64 views, similar to AnySplat [ref 1].\n\n\n* Missing comparison with per-scene optimization methods. It would strengthen the work to compare with per-scene optimization approaches such as BBSplat, given that BBSplat inspired this work. Such comparisons are also common in other works  exploring high-resolution feed-forward 3DGS, such as Long-LRM [ref 2] and LVT [ref 3].\n\n### References:\n\n* [ref 1] Jiang, Lihan, et al. \"AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views.\" arXiv preprint arXiv:2505.23716 (2025).\n* [ref 2] Ziwen, Chen, et al. \"Long-lrm: Long-sequence large reconstruction model for wide-coverage gaussian splats.\" ICCV 2025.\n* [ref 3] Imtiaz, Tooba, et al. \"LVT: Large-Scale Scene Reconstruction via Local View Transformers.\" arXiv preprint arXiv:2509.25001 (2025).", "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": "h64qPVADQp", "reviewer": "Reviewer_9Eok", "summary": "This paper introduces a new feed-forward framework called LGTM (Less Gaussians, Texture More) to solve a major problem: existing feed-forward 3D Gaussian Splatting (3DGS) methods cannot scale to high resolutions like 4K.\nThe Solution (LGTM): The method decouples geometry prediction from appearance prediction using a dual-network architecture.\n- A Primitive Network takes a low-resolution image (e.g., $512 \\times 288$) to predict a compact, fixed set of geometric primitives.\n- A Texture Network takes the high-resolution image (e.g., 4K) to predict detailed, per-primitive texture maps that \"paint\" high-frequency details onto the simple geometry.", "questions": "Perhaps the authors could include more visualizations, especially video demonstrations of the rendered views based on the predicted 3D-GS.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. A novel topic on feed-forward 3D-GS.\n2. A fair well performance compared with baseline methods.", "weaknesses": "1. High-resolution rendering requires accurate geometric prediction. However, the proposed method seems more like a trick—it projects a high-resolution image onto relatively coarse geometry. While this may work well when the input views have small viewpoint differences, it would be helpful if the authors could include additional experiments to evaluate the novel-view synthesis quality under different camera pose settings.\n2. The paper claims to \"decouple\" geometry and appearance 20, yet the architecture (Fig. 2) and method (Sec 4.2) show that the texture network $f_{texture}$ explicitly takes the primitive network's features $F_{prim}^v$ as input. This seems to be a one-way coupling rather than a full disentanglement.\n3. Although the paper provides a comprehensive analysis of the feed-forward 3DGS paradigm, recent per-scene optimization methods (e.g., Grendel-GS, CityGS-X) have already addressed high-resolution (e.g., 4K) rendering quite effectively. The reviewer understands that a direct comparison with these methods may be beyond the scope of this work, but it nonetheless raises the concern that the problem addressed here might not be as critical for the 3D vision community as implied.\n\nOverall, the paper tackles a relatively minor problem (i.e., 4K resolution rendering) using a fairly simple approach—mainly by adding an additional downstream head.", "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": "2JVkUVZ2An", "reviewer": "Reviewer_VCdN", "summary": "This paper proposed LGTM, a general extension for feed-forward 3D/2D Gaussian Splatting \nto enable 4K resolution novel view synthesis. Specifically, the proposed method decomposes the geometry and texture, utilizing a primitive branch to predict the geometry from low-resolution images and a texture branch to predict the high-resolution textures.", "questions": "Please see in weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-motivated.\n2. The paper is well-written and generally easy to follow.\n3. The experiments are throughout and the proposed method significantly boosts the performance of the baselines.\n4. The results is visually good.", "weaknesses": "1. Necessity of integrating 4K texture to 3DGS: another solution to get 4K renderings can be a general feed-forward 3DGS followed by an image super-resolution model. A comparison should be made between it and the proposed method.\n2. Evaluation: As the paper claims an immersive user experience, a non-reference perceptual metric such as Niqe [1] or Q-align [2] should be added as an evaluation metric.\n3. Line space issue in L.474.\n\n[1] Zhang, L., Zhang, L., & Bovik, A. C. (2015). A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8), 2579-2591.\n\n[2] Wu, H., Zhang, Z., Zhang, W., Chen, C., Liao, L., Li, C., ... & Lin, W. (2023). Q-align: Teaching lmms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090.", "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": "r7E1VxNONB", "reviewer": "Reviewer_T5YU", "summary": "The paper introduces LGTM (Less Gaussians, Texture More), a feed-forward framework for high-resolution (up to 4K) novel view synthesis with textured Gaussian primitives. The core idea is to decouple geometry from appearance: a primitive network consumes low-resolution inputs to predict a compact grid of 2DGS geometry (centers, scales, rotations, SH base color), while a texture network consumes high-resolution inputs to predict per-primitive color and alpha texture maps via image patchifying and learned projective texturing. They used a 2-stage training, achieving coarse-to-fine prediction. Firstly a rough geometric and then detailed textures.", "questions": "1. Do you have results/failure cases on scenes with thin/fine structures (hair, fabric wrinkles, foliage)? \n2. Can you compare against a full-resolution primitive predictor followed by (a) uniform decimation and (b) geometry-aware decimation/compression (e.g., curvature/edge cues)? What are the trade-offs?\n3. Were baselines retrained with strong multi-scale zoom/crop augmentation and full-resolution supervision, and evaluated under compute-matched budgets (memory/time)? If not, can you add this setting (and optionally test-time supersampling) to isolate the benefit of your texture mechanism?\n4. Can you report a simple consistency diagnostic (e.g., reprojection error or pose-jitter stress test) at high resolution?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper is well-writen and easy to understand. \n2. The results seem to be pretty good improvement on the base models. \n3. Pretty efficient algorithm and is friendly to small GPUs.", "weaknesses": "1. This work uses the few Gaussians from low-resolution images to serve as geometry probes, and paint the surfaces with higher resolution images. This is some what equivalent to first obtain all Gaussian points from the full resolution images (one may achieve this through similar way as e.g. Point3R[1]) and then uniformly **drop** most of the Gaussians. This relies on an important (but not necessarily true) assumption that real-world geometries are all smooth. This may causes the model to potentially fail when observing high frequency geometry details e.g. hairs, cloths, bumps, etc. No result has been given in that regard. Instead, a more clever way of downsampling / compressing Gaussians could be leveraged to both preserving geometry details & acceptable resources needed.\n2. The baseline models are never trained on high resolution images while it can be done: e.g. augmenting the original data with different zoom-in / zoom-out scale & crop to original resolution at train time and inference with full-resolution. The current large scale evaluation doesn't make too much sense.\n3. In L232-245 the authors use literally 1/4 page talking about a very elementary technique on assigning texture colors to Gaussian points, while ignoring some of the possibly more challenging topics such as how to handle view inconsistencies with less Gaussian points on high resolution images.\n4. The authors may have used the term \"primitive\" with different meanings without firmly defining any of them. As a result, it is hard to parse some parts of the manuscript. For example, what is \"per-primitive texture maps\", and how does it differ from \"2DGS primitives\"? The term \"Primitive Resolution\" repeatedly appears but is never explained.\n5. The use of mathematical symbols are not consistent, e.g. Eq 4. $f_{texture}$ seems like a mapping, while in L226, it becomes a \"feature\". The use of superscripts and subscripts are totally a mess.\n\n[1] Wu et al. Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory.  arXiv:2507.02863.", "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)