Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?
Authors: Karim Kassab
Antoine Schnepf
Jean-Yves Franceschi
Laurent Caraffa
Flavian Vasile
Jeremie Mary
Andrew I. Comport
Valerie Gouet-Brunet
Venue: ICLR 2026 Poster (2026)
Keywords: Tri-Planes
NeRF
3D
Latent
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Fused-Planes improves upon Tri-Planes for state-of-the-art resource efficiency when training large classes of objects, by leveraging shared representations in a latent space.

Abstract: Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures.
This has made them the prevailing choice to model large collections of 3D objects.
However, training Tri-Planes to model such large collections is computationally intensive and remains largely inefficient.
This is because the current approaches independently train one Tri-Plane per object, hence overlooking structural similarities in large classes of objects. 
In response to this issue, we introduce Fused-Planes, a novel object representation that improves the resource efficiency of Tri-Planes when reconstructing object classes, all while retaining the same planar structure.
Our approach explicitly captures structural similarities across objects through a latent space and a set of globally shared base planes.
Each individual Fused-Planes is then represented as a decomposition over these base planes, augmented with object-specific features.
Fused-Planes showcase state-of-the-art efficiency among planar representations, demonstrating $7.2 \times$ faster training and $3.2 \times$ lower memory footprint than Tri-Planes while maintaining rendering quality.
An ultra-lightweight variant further cuts per-object memory usage by $1875 \times$ with minimal quality loss.
Our project page can be found at https://fused-planes.github.io .

Published as a conference paper at ICLR 2026
FUSED-PLANES: WHY TRAIN A THOUSAND TRI-PLANES WHEN YOU CAN SHARE?
Karim Kassab 12, Antoine Schnepf" 1.3, Jean-en-veee Franceschi', Laurent Caraffa',
Flavian Vasile', Jeremie Mary', Andrew Comport 3, Valérie Gouet-Brunet!
Equal contribution
Criteo AI Lab, Paris, France
2 LASTIG, Université Gustave Eiffel, IGN-ENSG, F-94160 Saint-Mandé
3 Université Côte d' Azur, CNRS, 13S, France
ABSTRACT
Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice to model large collections of 3D objects. However, training Tri-Planes to model such large collections is computationally intensive and remains largely inefficient. This is because the current approaches inde- pendently train one Tri-Plane per object, hence overlooking structural similari- ties in large classes of objects. In response to this issue, we introduce Fused- Planes, novel object representation that improves the resource efficiency of Tri-Planes when reconstructing object classes, all while retaining the same pla- nar structure. Our approach explicitly captures structural similarities across ob- jects through a latent space and a set of globally shared base planes. Each in- dividual Fused-Planes then represented as a decomposition over these base planes, augmented with object-specific features. Fused-Planee showcase state-of- the-art efficiency among planar representations, demonstrating 7.2x faster train- ing and 3.2x lower memory footprint than Tri-Planes while maintaining ren- dering quality. An ultra-lightweight variant further cuts per-object memory us- age by 1875x with minimal quality loss. Our project page be found https:: / htt//ffueddeeeee
github.io.
K-Plancs Size: 410.1 MB PSNR: 26.78
fri-Planes Size: 1.5 MB
Fuscd-Planese Planes-ULW Size: 0.0008 MB PSNR: 28.44
Fused-Plant Size: 0.48 MB
PSNR: 25.97
PSNR: 29.69
Figure 1: Comparison of planar representations under the same budget. Our method achieves the best rendering quality and the best memory footprint among planar representations when training large classes 3D objects under a fixed time budget (7 minutes per object in this illustration). Fussed-Planes-UL.. designates the ultra-lightweight variant of Fused-Planes.
INTRODUCTION
Tri-planar representations (Chan et al., 2022; Fridovich-Keil et al., 2023) have recently driven sig- nificant progress in 3D computer vision, offering a unique advantage: they model 3D objects while remaining interpretable compatible with standard image-based models (e.g. CNNs), thereby unlocking new ways 2D vi- sion models can be used for 3DD tasks (Hong et al., 2024; Anciukevičius et al., 2023; Mercier al.,
2D structures due to their planar format. This planarity makes them

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Published as a conference paper at ICLR 2026
2025). Given that such applications are inherently data-intensive, the need to train large collections of Tri--laaes for 3D reconstruction has become increasingly prevalent (Cardace et al., 2024; Shue et al., 2023; Ju & Li, 2025), and a costly preliminary step in 3D research (Liu al., 2024; Wang et al., 2023, Sections 4.1 and 5). Yet, most existing methods overlook this costly 3D reconstruction step, focusing instead on the downstream tasks that planar representations enable. As such, using planar representations for large-scale 3D reconstruction remains largely suboptimal in terms of re- source efficiency, since existing methods train each Tri-Plane independently, ignoring the structural similarities that often exist across large object classes. This oversight leads to redundant compu- tations and inefficient memory usage. As a result, constructing dataset of Tri-Planes is currently unnecessarily computationally intensive.
In this work, we address the challenges associated with the computationally expensive task of large- scale 3D reconstruction using planar methods. We introduce Fused-Planes novel tri-planar rep- resentation that efficiently models large classes of 3D objects. Fused-Planes effectively reduces the resource costs associated with Tri-Planes by leveraging the structural similarities shared across mul- tiple objects. Additionally, Fused-Planes retains the planar property of Tri-Planes that has enabled their integration into existing pipelines, and thus retains their compatibility with recent approaches.
First, our Fused-Planes split an object representation into two separate components: the first "Mi- cro" component learns features specific to the object hand; the second "Macro" component is a learned decomposition over a set of base planes, where each base plane encapsulates structural similarities across the class of objects we want to reconstruct. Second, train Fused-Planes with 3D-aware latent space (Schnepf et al., 2025), which provides a continuous and structured represen- tation of objects, and accelerates the rendering and training of Fused-Planes.
The combination of these two cost-reducing components is essential. On the one hand, the latent space provides more effective representation for disentangling object-specific details from class- level structural similarities, making it easier to capture these similarities with the set of base planes. On the other hand, the micro-macro decomposition essential to eliminate the quality losses asso- ciated with using a latent space.
We conduct extensive experiments justifying these design choices and comparing our method with current planar representations when training on large classes of objects. Fused-Plang presents 7.2x faster training than Tri-Planes, while requiring 3.2x less memory footprint and retaining a similar rendering quality, thus establishing new state-of-the-art in efficiency for planar scene representa- tions. Moreover, an ultra- titr--igghteiihtt variant of -lannd trades off minor rendering quality for substantial gains in memory footprint: 1875x less than Tri-Planes. To the best of our knowledge, our work is the first to improve upon the resource efficiency of Tri-Planes.
2 RELATED WORK
Tri-Plancs, Tri-Planes (Chan et al., 2022) are widely used for modeling large collections of 3D objects and have attracted considerable attention due to their seamless integration with standard image-based models. In recent works, Tri-Planes are commonly used within a framework that in- volves solving two main tasks (Shue al., 2023; Ju & Li, 2025). The first task large-scale 3D reconstruction, which consists of training Tri-Planes to properly model large set of 3D objects. Once this prerequisite task completed, the Tri-Planes can be reshaped into 2D image-like tensors, an operation made possible by their planar structure, making them casily integrable with image- based models. Once trained and reshaped, Tri-Planes are applied to a second, targeted task, in conjunction with a chosen image-based based model. While recent studies have focused heavily ploring diverse targeted tasks such as editing (Ki et al., 2025), classification (Cardace et al., 2024), generation (Liu et al., 2024), and feed-forward reconstruction (Wang et al., 2023), the first large- scale reconstruction task itself remains inefficient and sub-optimal, which has inspired our research direction. A more detailed discussion of works using Tri-Planes for downstream tasks can be found in Appendix (Section A).
Compatibility of NeRF methods with image-based models. Since NeRF (Mildenhall et al., 2020), methods such Instant-NGP (Müller et al., 2022), TensoRF (Chen et al., 2022), and 3D Gaussian Splatting (Kerbl et al., 2023, 3DGS) have greatly advanced single-scene reconstruction. However, unlike Tri-Planes, these representations cannot be directly reshaped into image-like ten-
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sors. Recent works attempt to work around this by converting 3DGS scenes into 2D maps using various parametrization techniques (e.g. encoding a gaussian per pixel (Li et al., 2024)). However, this explicit parameterization either (i) requires 3D-to-2D unwrapping techniques (e.g.. UV maps (Hu et al., 2024; Pang et al., 2024) or Morton-order mappings (Jiang et al., 2025)) to preserve spa- tial semantics across different sides of an object, (ii) damage image-like spatial semantics, since adjacent pixels may correspond to spatially distant Gaussians (Szymanowicz et al., 2024). In con- trast, Tri-Planes require no such preprocessing. They are fixed-size, fully structured, and consistent across scenes, making them directly compatible with standard amage-based architectures.
These properties have made them the most adopted approach in large-scale 3D reconstruction, and motivate our focus on adopting purely planar representations, without introducing auxiliary non- planar components (Wu et al., 2024a).
Our work aims to address the inefficiencies of large-scale 3D reconstruction with Tri-Planes. First, we design Fused-Plane ities in object classes. Second, we train Fused-Planee as latent NeRFs, facilitating the learning of our shared representations. These design choices lead to substantial reductions in both training time and memory footprint.
be a tri- tri-planar shared representation that captures the structural similar-
Shared representations. Shared representations denote approaches that model multiple objects by utilizing common components. These representations encode an abstraction of set of objects, effectively capturing dataset-level information such as structural similarities and differences among objects. For example. Jang & Agapito (2021) represent multiple objects of the same class within single NeRF (MLP) by conditioning it on distinct latent codes for shape and appearance, which al- lows shape and appearance to be edited independently. Similarly, Schwarz et al. (2021); Niemeyer & Geiger (2021) adopt a shared representation implemented within a GAN framework, which enables the generation of novel objects and scenes. Notably, shared representations have been employed to reduce memory footprint when modeling multiple 3D objects. For instance, Singh et al. (2024) encode multiple scenes into a single NeRF using learned pseudo-labels, thereby reducing memory footprint. However, their method cannot scale beyond 20 scenes. Our work also utilizes shared representations for resource efficiency, but remains scalable to thousands of objects while reducing both memory footprint and training time. To the best of our knowledge, our method is the first to explicitly integrate shared representations with planar structures.
Latent NeRFs. Latent NeRFs involve training neural scene representations within the latent space of an image autoencoder, rather than directly using raw RGB images. Several recent works have utilized Latent NeRFs for 3D generation (Metzer et al., 2023; Seo et al., 2023; Ye et al., 2023; Chan et al., 2023), scene editing (Khalid et al., 2023; Park et al., 2024), and scene reconstruction AAumentado-Armstronn
et al., 2023) with improved quality. Recently, Schnepf et al. (2025) em- ployed latent NeRFs to accelerate NeRF training. Their approach enables training various NeRF architectures within a 3D-aware latent space, resulting in substantial speed-ups but at the expense of notable degradation in rendering quality. Our work builds upon Schnepf et al. (2025) by train- ing our proposed Fused-Planes representation in a 3D-aware latent space. However, unlike Schnepf al. (2025) who pre-train generic latent space for all NeRF representations, we train the 3D-aware latent space jointly with our scene representations, which proves essential for preserving rendering quality. This improvement enables us to achieve substantial speed-ups without quality compromises.
METHOD
Our method efficiently reconstructs large collections of 3D objects using tri-planar representations. Section 3.1 presents our novel Fused-Planes representation, which splits an object representation into an object-specific "micro" component and a "macro" component derived from shared base representations. These base representations are trained on the entire dataset, allowing to capture global structural patterns shared by the objects being reconstructed. To achieve this, we train the set of Fused-Planee in a jointly learned 3D-aware latent space, which encodes the target objects in compact and well-structured space, thereby facilitating the learning of shared patterns with our base planes. Section 3.2 describes our training procedure for Fused-Plane and the 3D-aware latent space. Figure presents an overview of our method.
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(a) Fused-Plance representation
(b) View synthesis
Fused-Plane
Shared decoder
Output view
Volume Rendering
T.mic
R
D
W
T
mac
T₃
Zi,j
writted
Dij
Object-specific
C (latent)
CRGBB
planes & weights
(c) Supervision
GT view
Shared basc plancs B
B
D
Zi,j
B1
B
Fij
ii.
Figure 2: Method overview. A set of Fused-Planes { reconstructs class of 3D objects {Ot} from their GT views {Ii.)). where i and respectively denote the object and the view indices. For clarity, only one Fused-Planes plane T.mic which captures object-specific information, and macro plane T.mar computed via weighted summation over set of shared base planes B. This base captures class-level information objects. (b) View synthesis is performed in the latent space of an
shown. (a) Each Fused-Planes T; is formed from a micro
like structural similarities auto-encoder (F.g. Dw) via classical volume rendering. The rendered latent image 3 (low resolu- tion) is decoded to obtain the output RGB view (high resolution). (c) The Fused- -haness components (i.e. T.mic B, W;) and the autoencoder are supervised with three reconstructive losses.
Notation. We denote O (O1,....ONt On3 large of N objects drawn from common distribu- tion. Each object O; = {(ri.j (1))) consists of V posed views. Here, Di.j and Ci respectively denote the j-th view and camera pose of the i-th object O,. We denote T = ( TN} the set of Fused-Planes representations modeling the objects O.
3.1 FUSED-PLANES ARCHITECTURE.
Pre-requisite: Tri-Planes. Tri-Plane representations (Chan et al., 2022) are explicit-implic scene representations enabling scene modeling three axis-aligned orthogonal feature planes, each of resolution K x K with feature dimension F. To query a 3D point x E RR3 it is projected onto each the three planes to retrieve bilinearly interpolated feature vectors. These feature vectors are then aggregated via summation and passed into small neural network with parameters to retrieve the color and density, which are then used for volume rendering (Kajiya & Von Herzen, 1984).
Notably, Tri-Planes
be represented 2D structures by reshaping them into K x K images with 3F channels. As such, they can be seamlessly integrated in image- pipelines. This planar property has been fundamental for their widespread adoption, and it is preserved in Fused- mused-Planes.
Architecture of a Fused-Planes. Fused-Planes is novel planar 3D representation that builds upon Tri-Planes. A Fused-Planes splits planar representation into object-specific features, and class-level features, which allows to learn common structures across the large set of objects. Specif- ically, Fused-Planee representation T, object O is composed of "micro" plane T. integrating object-level information, and "macro" plane T.mac that encompasses class-level information:
T; Timic @ T.mac
(1)
where concatenates two planar structures along the feature dimension. We denote by Emic the dimensionality of local features in T.mic and by Fmane the dimensionality global features T.mac, with the total dimensionality of features in T; being F Full + puse.
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Published as a conference paper at ICLR 2026
The micro planes T.mic are object-speciffe and are hence independently learned for every object. The macro planes T.maa represent globally captured information that is relevant for the current object. They are computed for each object from shared base planes B {Bx MM by the weighted sum:
M
T.mas W,B
=
W Bk
where W; are learned coefficients for object Oz. The base of planes (B₂) is shared among objects and capture class-level structural similarities. With this approach, the number of miero planes is equal the number of objects N, while the number of macro planes M is a constant hyper-parameter. We take M > in order to capture diverse information, which our experiments showed be beneficial for maintaining rendering quality, and M < N. Overall, decomposing Mused-Planee into micro and macro components reduces the number of trainable features per-object compared to traditional Tri-Planes, thus accelerating training and reducing total memory footprint.
We propose ultra-lightweight (ULW) variant of our method with /uiic (only macro planes), where we achieve substantial savings memory footprint at the expense of slight reduction in rendering quality.
Mised-Plancs-ULN.
3D-aware latent space. While Tri-Planes are traditionally used to model objects in the RGB space, we train Fused-Planes in the latent space of an image autoencoder, defined by encoder Eg and a decoder D This is because a high-dimensional RGB space lacks structure, making it poorly suited for effectively capturing structural similarities. contrast, 3D-aware latent space (Schnepf et al., 2025) provides a structured and continuous encoding of the objects, which is, as proven by our ablations, more suited for disentangling structural similarities from object-specific details. Additionally, this latent space allows for reduced rendering resolution, which alleviates the cost of volume rendering and contributes to accelerating our training. In practice, we train our 3D-aware latent space jointly with our Fused-Planes, which tailors it specifically for our decomposed object representation.
At inference, given camera pose Cj. we render latent Fused-Plane T; as follows:
و = R. (T;,
... = D& (Z..))
where R. is the Hised-Plane renderer with trainable parameters a, ž.,5 is the rendered latent image, and žijj is the corresponding RGB decoded rendering.
3.2 TRAINING A LARGE SET OF FUSED-PLANES
This section outlines our training strategy to learn a large set of objects. In brief, we jointly train the set of Fused-Planes and the 3D-aware latent space. Figure 2 provides overview of our pipeline.
Training a set of Fused-Planee jointly with the 3D-aware latent space. We train the set of Fused-Planes T to reconstruct the set of objects O from posed views. As described above, we conduct this training in 3D-aware latent space in joint manner. To do so, we adapt the 3D regularization losses from Schnepf et al. (2025). Note that our 3D-aware latent space differs from the one in (Schnepf et al., 2025), it is subject to additional training constraint coming from our micro-macro decomposition. This allows us to obtain a latent space that is not only 3D-aware, but also adapted our Fused- Planes representations.
We supervise a Fused-Plaaes T; and the encoder Es in the latent space with the loss Clatentt;
(latent) Lij
(Ø, T3) = | - 11.
4))
where 21,j = (x(,)) is the encoded ground truth image, Zι,J Ra (Ti, Ct.)) is the rendered latent image. and T; = Trmic @ Tunac This loss optimizes the encoder parameters and the Fused- parameters to align the encoded latent images 2i, and the rendering ět.j. We also supervise T, and the decoder Dy in the RGB space via ((RBB),
(RGB) [
(s), T:) = || B.
(5)
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Published as a conference paper at ICLR 2026
K-Planes
Training 75.35 min Sizc: 410.17 MB
Tri-Planee Planes
Troining; min Siae: 1.5 MB
Fused-Planes-Uu Planes-UUUU ULW
Training: min Size: 0.0008 MB
Fused--Paaees
Training: 8,92
Size: 0 MB
Ground Truth
Figure 3: Qualitative comparison. We show comparisons of method with other planar scene representations for NVS on held-out test views. Our method achieves the fastest training with the lowest memory footprint, while maintaining a comparable rendering quality.
where Tiij is the ground truth image, and ãūاj = (Z.,)) ) is the decoded rendering. This loss ensures good rendering quality when decoded the RGB space, and finds the optimal decoder for this task. Finally, adopt the reconstructive objective [(se) supervising the auto-encoder:
((ae) (0, v) = |xi. 2r.5 12
(6)
where ãi,5 = (E.((())) is the reconstructed ground truth image.
Overall, full training objective is composed of the three previous losses summed over O to optimize the set of Fused-Planee T, the encoder Es, and the decoder D
N
X (latent) (latent) Cij (Ø. (i. + X (RGB) (RGB)
Lij (v. x C (0.
(ac)
where (latent), A (RGB), and (ae) are hyper-parameters.
By the end of this training. the set of Fused-Planes including the base planes are learned and effectively model the objects in O. Additional object representations could still be trained by utilizing the frozen shared components. For more implementation details, we refer the reader the Appendix (Section B and Algorithm 1).
4 EXPERIMENTS
Task. As discussed in Section our goal is reduce the resource costs of planar representations in large-scale 3D modeling. To establish the practical utility of our representation, it must satisfy two criteria: (i) accurately represent the types 3D objects typically modeled with Tri-Planes, and (ii) demonstrate competitive resource efficiency relative to the Tri-Planes baseline. Regarding 3D modeling performance, we adopt the standard evaluation protocol for 3D representations and assess method the task of 3D reconstruction via Novel View Synthesis (NVS). For resource efficiency, we measure the per-object training time and memory footprint when modeling large object classes.

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Published as a conference paper at ICLR 2026
Table 1: Comparison with planar methods. Fused-Planes reduces the quality gap between Tri- Planes and K-Planes, while requiring three orders of magnitude less memory footprint, and having significantly faster training. thus establishing a new state-of ofte--of-the-rt in efficiency for modeling large object classes with planar representations.
Training (mini
ShapeNer datnsets
Basel Faces
Planar
(MB)
PSNR
SSIM
1.PIPS
PSNR
SSIM
1.PIPS
K-Plancs (Fridovich-Kon) al., 2023)
75.35
410.17
30.88
0.956
0.043
40.23
0.991
0.005
Tri-Planes (Chan et al., 2022)
64.32
1.50
28.15
0.919
0.121
36.47
0.980
0.013
Pused-Plancs-UU (ours)
7.16
0,0008
29.02
0.937
0.092
33.96
0.955
0.010
Fused- Planes (ours)
30.47
0.957
0.042
37.24
0.973
0.006
Table 2:
Comparison with methods using shared representations. Fused-Planes demonstrates more favorable NVS quality and per-object resources requirements compared to CodeNeRF.
Training (min)
Size (MB)
ShapeNet datasets (avg)
Basel Faces
Planur
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
CodeNeRF (Jang Agapito, 2021)
X
14.96
0.0026
28.34
0.930
0,121
35,46
0.972
0,010
CodeNeRF-A (our adapeation)
x
9.54
0.0026
26.99
0.915
0.126
35.44
0.971
0.010
Fused-Planes-ULW (ours)
7.16
0.0008
29.02
0.937
0.092
33.96
0.955
0.010
Fused- Planes (ours)
8.96
0.48
30.47
0.957
0.042
37.24
0.973
0.006
Table 3: Report of standard NeRF methods. To provide the reader with a broader perspective, report the metrics of other well-established NeRF methods. As discussed, these methods do not share the same architectural versatility as planar methods and are designed for different objectives.
Training (minn)
Size (MB)
ShapeNet datasets (avg)
Basel Faces
Planur
PSNR
SSTM
T.PIP5
PSNR
SSIM
T.PIPS
Vanilla NeRF (Mildenhall et al., 2020)
X
636.8
22,00
35.85
0.977
0.027
42.91
0.996
0.001
Instant-NGP (Maller et al., 2022)
X
7.52
189.13
34.06
0.973
0.022
36.54
0.98
0.009
TensoRF (Chen al., 2022)
x
68.92
208.32
36.74
0.985
0.013
40,66
0.991
0.004
3DGS (Kerhl et al., 2023)
x
9.37
27.66
32.95
0.975
0.043
43.06
0.995
0.002
Musod-Planes-ULV
7,16
0.0008
29.02
0.937
0.092
33,96
0.955
0,010
Pused-Plancs
8.96
0.48
30.47
0.957
0.042
37.24
0.973
0.006
Evaluation Protocol To evaluate the NVS quality of the learned objects, we compute the PSNR (1). SSIM (1) and LPIPS (Zhang et al., 2018, 4) between never-seen reference views and corre- sponding NVS views. To evaluate the resource requirements, we report per-object training time, per-object memory footprint (excluding shared components), and total memory footprint. Training times are measured using single NVIDIA L4 GPU.
Baselines. We compare Fused-Planes with three distinct lines of work. First, the central compar- ison is with planar scene representations, specifically Tri-Planes (Chan et al., 2022) and K-Planes (Fridovich-Kad Keil et al., 2023), the only current works having planar structures. Tri-Planes is our base- line architecture and hence is our main point of comparison. K-Planes extend Tri-Planes to improve rendering quality by utilizing multi-scale planes, sacrificing on memory footprint, and most impor- tantly the explicit 2D structure, as multi-scale planes cannot be directly reshaped into a single 2D structure. Second, we compare Fused-Planne with works utilizing shared representations. From this category, we consider CodeNeRF (Jang & Agapito, 2021), a recent non- non-planar method utilizing a shared NeRF conditioned by latent vectors. We also compare with CodeNeRF-A. our adaptation of CodeNeRF designed to improve efficiency (more details Section F). Note that we do not compare with C3-NeRF (Singh et al., 2024) as their approach does not scale beyond 20 scenes. Third, and to provide the reader with a larger perspective, we report the performance of other well-established non-planar scene representations (Mildenhall et al., 2020; Müller et al., 2022: Chen et al., 2022; Kerbl et al., 2023), using their Nerfstudio (Tancik et al., 2023) implementations. While these meth- ods are designed to model scenes individually with high fidelity. they are not readily integrable models as planar methods, and therefore lack their architectural versatility. As
with image- such, they are not our primary point of comparison, but included to provide broader context.

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Published as a conference paper at ICLR 2026
Table Multi-class training. The first three rows correspond to single-class training, where sep- arate Fused-Planes model is trained for each individual class. The remaining rows report the results of multi-class training, where single Fused-Plasd Planes model trained jointly on multiple classes. The results show that Fused-Planes is applicable to multi-class data and continues to outperform Tri-Planes in this setting.
Speakers
Sofas
Purasture
Cars
Classes
PSNK
SSIM
LPIPS
PSNK
SSIM
LPPS
PSNK
SSIM
LPPP
PSNK
SSIM
LPIPS
Pused-PPlnce
0.960
Fused-Plames-T1W
0.941
0.931
0.933
28.71
0.943
Tri-Planss
27.02
0.909
0.134
28.48
0.921
0.103
27.42
0.894
0.210
29.69
0.953
0.036
Fused-Planee
30.03
0,953
0.033
30.37
0.953
0.033
Prsed-Planes-LIM
28.63
0,925
0.108
28.93
0.951
Pused-Planes
0.950
30.31
0.955
SereddPlanne-LLAe ULW
29.30
0.957
0.88
29.33
0.957
0.064
29.47
0.942
0.118
Pused-Planes Planes Fused- PU
29.72
0.951
0,055
29.70
0.943
0,038
29.79
0.951
0,073
29.15
0.952
0,000
28.12
0.924
0.110
26.34
0.923
0.084
28.54
0.927
0.154
27.73
0.953
0.074
Datasets & Experimental Details. We evaluate our method on large-scale 3D data. Consistently with Tri-Planes and CodeNeRF, we use ShapeNet (Chang et al., 2015), from which we take four categories: Cars, Furniture, Speakers and Sofas. Additionally, we adopt the large-scale front-facing Basel-Face dataset (Paysan et al., 2009). More dataset details can be found in Section C. In our experiments, we train a set Fused-Planes to reconstruct
= 2000 objects. We use planes of dimensionality K K F, where K 64 and F = 32 for all planar representations. For Fused- Planes, we take guiic = 10, Fma = 22, and M = 50. For mnsee-Plancss-ee
we take p'mic = 0, piac = 32, and M 50. We detail our apper-parameters in Section G. We adopt the pre-trained VAE from Stable Diffusion (Rombach et al., 2022) as initialization for our VAE.
4.1
MAIN RESULTS
Main results appear in Tables and Figures and 3. Detailed results are available in Section D.
Compared to other planar scene representations (Figure I and Table 1), Fused-Planes exhibits a significant reduction in resource costs, demonstrating 7.2x faster training and 3.2x less memory footprint than Tri-Planes, and 8.4x faster training and 854x less memory footprint than K-Planes. improves rendering quality over Tri-Planes while reducing the gap with K-Planes, but without K-Planes' orders-of-nng maagnnnude higher memory cost multi-scale complexity. Fused-Planes- ULW trades off minor rendering quality for substantial gains in memory footprint: one object requires 1875x less memory footprint than Tri- Tri-Planes, and 512 000x less memory footprint than K-Planes. Furthermore, Figure 4 illustrates the evolution the resource requirements as the number of objects increases. Moreover, detailed breakdown of the memory footprint of Fused-Planes can be found in the Appendix (Table 15). All in all, Fused-Planes establishes a new state-of-the-art in terms of resource efficiency for planar scene representations.
As for other methods utilizing shared representations (Table 2), Fused-Planes and Fused-Planes- ULW showcase up 2x faster training times, and an improved rendering quality. Fused-Planes- ULW also requires less memory footprint per-object.
For broader context, we report results other well-established non-planar NeRF methods (Table and Figure 5). Fused-Planes, like all other planar representations, showcases lower rendering quality, which is acceptable trade-off planar methods have different primary objective (Section 2).
4.2 RESULTS ON MULTI-CLASS TRAINING
Table 4 reports set of experiments in which Fused-Planes and Fused-Planes-ULLW are trained on datasets containing scenes from multiple object classes. Specifically, we introduce three new datasets that combine two, three, and four object classes, composed from our initial object cate- gories. Specific details about the construction of these datasets are available in the Appendix (Sec- tion C). The results demonstrate that Fused- is applicable multi-class data and continues to outperform Tri-Planee this setting. Furthermore, a minor reduction in quality appears as more classes included, reflecting the increased scene diversity that shared base planes must capture.
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Table 5: Ablation study. Comparison of NVS quality and per-object resource costs for different ablations of our method on ShapeNet Cars. sused-Planee outperforms all of its ablations. Fused- Planes-ULV trades off minor NVS quality for substantial savings in memory footprint.
Latent Space
Micro
Macro Planes
Training (min)
Size (MB)
PSNR
SSIM
LPIPS
Planes
Fused-Planes (M
1)
8.48
0.48
27.69
0.942
0.042
Pused-Planes (Micro)
12.84
1.50
27.64
0.941
0.040
Fused-Planes (RGB)
X
/
/
63,52
0.48
27.71
0.942
0.044
Tri- Pii-Planes
X
/
x
64.08
1.50
28.56
0.953
0.035
Fused-Planes- Planes-ULW
x
7.16
0.0008
27.51
0.935
0.063
Fused-Planes
8.92
0.48
28.64
0,950
0.037
Pusod-Ploues
Memery Clet (1:D))
1000.0
Plans- ULW
K-Planes
Thining Tine
Tii-Pacn Pacces
200.0
360
124.7
28 500
1000
1200
2000
500
1000
1500
2000
N
Figure 4: Scaling the number of objects using planar methods. Evolution of the total training time (left) and total memory footprint (right) when scaling the number of objects (N). As K-Planes is barely visible (right), present in Figure magnified version of the memory cost plot.
Importantly, this effect is small, and the resulting quality remains on par with above that of Tri- Planes. Note that training speed and memory usage remain unchanged multi-class training with respect to the single-category models.
4.3 ADDITIONAL RESULTS
In the Appendix, Figure 10 illustrates subset of our large-scale results for completeness. Sec- tion D.3 provides an ablation study on the number of base planes, in which we show that M = 50 is the most effective option for Fused-Planes. Section D.4 presents rendering speed analysis show- ing that Fused-Planes achieves substantially faster rendering than Tri-Planes, while being compet- itive with the other baselines. Section D.5 provides experiments utilizing low-budget VAE with Fused-Planes, showing that our method exhibits low-sensitivity to the specific VAE initialization. Section D.6 presents comparison of the total resource costs across our baselines, which shows that Fused-Planes presents competetitve training times, and is the fastest planar method. In terms of memory, Fused- Planees and
Planes-ULW are the most lightweight methods. Section E anal- yses our base- paseeplanes and the representations they learn. In brief, our base planes can be grouped into two categories: semantic planes that clearly encode object-level structures, and residual planes that capture finer intra-class variability. Moreover, we visualize the values of the weights W; for two different objects, which shows that for each object, a few base planes are dominantly activated, while other planes contribute minor adjustments. Finally, we also present an experiment in which we interpolate between two learned weights in Figure 9, showing that we can transition smoothly from one scene to another. Per-object NVS results and visualizations are available in Tables 17 to 21 and Figures to 15.
4.4 ABLATIONS
To justify our design choices, we present an ablation study of our method (Table 5). "Fused-Plame (M 1)" reduces the shared base planes to a single plane. It demonstrates slight degradation of quality compared to Fused-Planes. highlighting the necessity for set of base planes. "Fused- Planes (Micro)" eliminates the Macro component of Fused-Planes (i.e. F'mac = 0), and therefore the shared components. It exhibits lower quality compared Tri-Planes, which is in line with the degradations seen Schnepf et al. (2025) for latent NeRFs. In contrast, our full model avoids such issues, underscoring the benefits of shared representations within the latent space, both in quality and

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K-Plears TensoRF
INGP
307
PSUR $4.00
JDGS
201
Vandlla Nettll
Fused- Planes (ours)
Tri-Pllaee Plaenes PUNIE 2015
Size
10
10
CodeNeeR
Non-Plazar
Rused-Planes- Planes- Planes-UIX (ours)
Planar
Plbllc
10
Training Time (min)
Figure 5: Resource costs overview. To reconstruct a large class of objects, one would consider three options: many per-scene models (e.g. INGP, 3DGS, or planar methods), multi-scene method (e.g. CodeNeRF), or Fused-Planes. Our method presents the lowest per-object training time and memory footprint among all planar representations, while maintaining a similar rendering quality. Circle sizes represent the NVS quality.
memory efficiency. "Fused-Planes (RGB)" ablates the latent space and trains Fused-Planes in RGB space. It exhibits lower quality compared to Tri-Planes, and to our full model. Therefore, it shows the necessity of the latent space for making shared representations work effectively. It also highlights the speed improvements enabled by the latent space. "Tri-Planes" is equivalent to ablating both the latent space and macro planes, which presents significantly higher resource costs and similar quality. In summary, ablations show that both the latent space and shared representations needed concurrently to avoid quality degradations and minimize resource costs.
4.5 LIMITATIONS
Tri-Planes well-suited for object-centric scenes. However, they exhibit limitations in capturing fine details and handling unbounded scenes, which are characteristic of real-world environments. As such, Tri-Planes cannot be used reconstruct scenes such the ones used the NeRF paper (Mildenhall et al., 2020) or in the Mip-NeRF 360 dataset (Barron et al., 2022). More precisely, to capture fine details, one would need to greatly increase the resolution of each of the Tri-Planes fea- ture grids, leading to sigmificant increases in memory footprint and computation, which undermines compactness that makes Tri-Planes attractive. Moreover, Tri-Planes assume that the scene fits in bounded volume, which complicates the modeling of distant backgrounds often present in real scenes. Some methods (Wu al., 2024b; Lee al., 2024; Yan al., 2024) sidestep these limitations by using tricks like utilizing multiple Tri-Planes for large scenes or by modeling only density and relying on other tools for textures. These approaches are beyond this paper's 's scope.
Since our method adopts the same architecture as Tri-Planes, it also inherits their limitations. Even so, Tri-Planes have been widely adopted (Section 2), as their planar design provides practical ad- vantages despite these drawbacks. Our contribution advances this line of work by proposing more efficient way to train planar methods at large-scales, while improving the quality of Tri-Planes.
5 CONCLUSION
In this work, we introduced Fused-Planes, a novel planar object representation that advances the state of the in resource-effcient planar 3D modeling and reconstruction of large object classes. This is achieved by shifting away from the traditional approach of reconstructing each object in isolation, and instead exploiting the shared structural similarities within object classes using shared base representations in a specially designed latent space. We showed that Fused-Plance significantly reduces required resources compared to current planar representation, while maintaining rendering quality. Given the recurrent challenges associated with training large-scale planar scene represen- tations, we hope that our contribution will facilitate this task, and make research in image-based models for 3D applications accessible.
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REPRODUCIBILITY STATEMENT
We have taken several measures to ensure the reproducibility of our findings. 'hee paper includes the necessary implementation details and hyperparameter settings in order to reproduce results. Additionally, the complete open-source code can be accessed via our project webpage. Together, these resources should allow researchers fully reproduce and extend our findings.
ACKNOWLEDGMENTS
We would like express sincere gratitude to Thibaut Issenhuth and Song Duong for their thoughtful feedback and insightful comments on this paper. Their careful reading and construc- tive suggestions helped clarify key arguments and significantly improve the overall quality of the paper. We are also deeply thankful to Vicky Kalogeiton and Xi Wang for their valuable ex- ternal feedback. Their fresh perspective encouraged us rethink several aspects of our work and strengthened the paper in meaningful ways.
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A WORKS UTILIZING TRI-PLANES FOR TARGETED TASKS
In this section, we highlight representative works that utilize Tri-Planes for varied targeted tasks.
Editing, Bilecen et al. (2025); Ki et al. (2025) use Tri-Planes to perform 3D-aware editing, based on conditioning images. Such editing allows combine the overall appearance of one object with selected characteristics of a different object.
Feed-forward reconstruction. Hong al. (2024); Wang al. (2023); Sun al. (2024) propose feed-forward image-to-3D pipelines: they infer Tri-Planes from single images by switching the output modality of image-based models Tri-Planes.
Generation. Shue et al. (2023); Chen et al. (2023); Anciukevičius et al. (2023) build a diffusion framework around Tri-Planes, treating them if they images with more channels, which enables 3D object generation using image generative models.
Classification. Cardace et al. (2024) leverage Tri-Planes classify neural fields without creating the explicit signal (i.e. without rendering), and highlight the rich semantic signal present in Tri-Planes, well their ease of use with standard neural architectures.
B ADDITIONAL IMPLEMENTATION DETAILS
This section presents some additional details regarding the training of Fused-Planes, namely its warm-up stage and the early stopping of the encoder.
In practice, we use two regimes of optimization to gain some computational efficiency. In fact, we notice that the encoder Es converges before the set of N 2000 PlanesPlaaes. Hence, continuing to optimize it would be unnecessary. As such, we jointly train the encoder only with a subset Ti (Ti 1,-.. TN, } of Fused-Planes (regime 1), before learning the remaining Fused-Plannes To (TN +...TT' with a frozen encoder (regime 2). We set N1 500. For completeness, we also detail the warm-up stage of the Fused-Planes the start of regimes and 2). This warm-up stage is necessary just after the random initialization of Fused-Planes, to avoid back-propagating random gradients into the auto-encoder.
Regime 1. We start by warming-up Ti with the following objective:
(latent)
min T1.0
(o,T,,a)
(8)
==
We then optimize the Fused- Planes in Tı, the encoder Eg and the decoder Dy using Equation (7), recalled here:
min Thaa
A (laennt) (lateent)
(o, a)
+ (RGB) (RGB)
(vb, Tú, a)
iij
(ac) (((e) (ae)
+
i.j
(b)
Regime 2. Similarly to the first regime, we start by warming-up To with the following objective:
N
min 72.a
Σ
Σ
(latent)
(0, a))
(10)
1
We then optimize the Fused-Plance in Tz, but only (RGB) is needed, as the encoder no longer requires training. We keep fine-tuning the decoder Dr- The objective is:
min T2.0.5
Σ
Σ
(RGB) (RRGB) (v) a)
L.j
(11)
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Practically, achieve the previous objective using mini-batch gradient descent. Details can be found in Algorithm 1. The rendering quality remains the same between the two regimes, as illus- trated in Tables 6 and 7.
C DATASET DETAILS
We use ShapeNet (Chang al., 2015) and Basel-Face (Paysan al., 2009) evaluate the novel view synthesis performance of the object representations.
The ShapeNet dataset is a large-scale, annotated collection of 3D models covering various object categories, widely used for 3D applications. We use four distinct object categories to evaluate our method: Cars, Furniture, Speakers and Sofas. For each ShapeNet object, we render V 160 views, sampled from the upper hemisphere surrounding the object.
The Basel-Face dataset contains more than 1000 distinct face models. The faces are generated from a 3D morphable face model with 199 principle components. For faces, we take V = 50 front-facing views.
All views are rendered at resolution of 128 x 128. In all our experiments, we use 90% of the views for training and 10% for evaluation.
Multi-class datasets. To assess our method in multi-class settings, we construct four new datasets that combine two, three, and four object categories from our original collection. Each dataset con- tains 2,000 objects. The first dataset, Speakers & Sofas, includes 1,000 speakers and 1,000 sofas. The second dataset, Speakers, Sofas & Furniture, is composed of 667 speakers, 667 sofas, and 667 furniture objects. The final dataset, Speakers, Sofas, Furniture & Cars, contains 500 objects from each of the four categories. We ensure that the scenes used for evaluations the same across datasets, in order to have rigorous comparisons.
D SUPPLEMENTARY RESULTS
D.1 QUALITATIVE RESULTS
We showcase subset of our large-scale results on ShapeNet cars in Figure 10.
Additionally, we present in Figures 11 to 15 additional qualitative comparisons across all the meth- ods discussed in our experiments (Section 4). Fused-Plane demonstrates similar visual quality to state-of-the-aat methods.
D.2 QUANTITATIVE RESULTS
Regarding rendering quality, present per-scene NVS metrics in Tables 17 to 21.
Regarding resource costs, the shared components (i.e. encoder, decoder and base planes) Fused- respectively require total of 360.75 MB and 384.19 MB of storage capacity. Note that we do not include the memory footprint of these components our analysis, as this overhead is constant regardless of the number of objects, and hence negligible in large-scale settings. This memory cost is illustrated in Figure 4 and magnified in Figure 6, focusing on the range [0,100],
Planes and Peeeed-Paans--UTi
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Table 6: Quantitative comparison. NVS performances on ShapeNet Cars in both regimes of our training.
ShapeNet Cars
Regime
Regime 2
PSNRT
SSIM
LPIPS.
PSNR
SSIM
LPIPS_
Tri-Plancs (RGB)
28.49
0.9539
0.0291
28.58
0,9505 0.9496
0.0360
Fused-Plame
28.14
0.9505
0.0301
28.77
0.0383
Table 7: Quantitative comparison. NVS performances Basel Faces in both regimes of our training.
Basel Faces
Regime
Regime
PSNR
SSIMI
LPIPS
PSNR
SSIM1
LPIPS
Tri-Planes (RGB)
36.82
0.9807
0.0122 0.0062
36.35
0.9787
0.0129
Fused-Plance
36.17
0.9678
36.99
0.9712
0,0056
Memory Cost (MB)
Fused-Planes
1000.0
Fused Planes ULW
K- Planes
Thi- Thi-Planes
500.0
360.75
0.0
50
100
N
Figure 6: Memory costs. This figure presents the memory costs depicted in Figure 4 within the
range N [0, 100).
D.3 ABLATION STUDY ON THE NUMBER OF BASE PLANES
Table presents study of the effect of number of base planes M on the resource costs and rendering quality of Fused-Planes. The reported NVS quality metrics are averaged on ShapeNet Cars scenes. The table shows that rendering quality varies only minimally across different values of M. We select M 50 because offers the best quality while maintaining similar training time and memory usage. Table presents an extension of this study in the context of datasets mixing multiples objet classes. The results show that Fused-Plane number classes, indicating that class diversity can be effectively captured with 50 base planes, as increasing M beyond 50 does not provide additional benefit.
shows similar performances when increasing the
D.4 ANALYSIS OF RENDERING SPEED
Table 10 presents the computational costs of computing one Fused-Planes (Equations (1) and (2)) and compares relative rendering times. As this operation only needs to be done once when loading Fused-Planes representation, it is largely negligeable compared to the overall rendering time.
Table 11 presents comparative study of the rendering speed of our method and its baselines. in terms of frames per second (FPS). Fused-Planee achieve significantly better rendering speeds com- pared to its planar baselines.
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Table Study the number base planes M. We select M 50 offers the best quality while maintaining similar training time and memory usage.
Per-Scene Training Time (min)
Total Memory 2000 Scenes (MB)
PSNR
8.60
1276
29.89
20
8.61
1291
30.02
Fused-Plancs
50
8.92
1322
30.27
75
8.99
1348
29.62
Table 9: Effect of M multi-class reconstruction. We report PSNR values for NVS evaluation, averaged over multiple scenes of the same category (cars and speakers), when jointly learning one or four classes. Increasing M beyond 50 does not provide additional benefit, indicating that class diversity can be effectively captured with M = 50 base planes.
One class
Four classes
M
Cars
Speakers
Cars
Speakers
50
30.27
29.99
29.15 28.58
29.72
Fused-Plane
75
29.62
29.46
29.20
Note that in both tables, the reported rendering time for Fused-Plane includes (i) the volume- rendering step and (ii) the decoding step that converts the rendered latent representation into an RGB image.
Table 10: Computational overhead rendering speed analysis. We compare the computational cost of Fused-Plance against RGB Tri-Planes for rendering single frame and multiple frames. Rendering with Fused-Planes is approximately twice as fast. Moreover, the overhead introduced by the Fused-Planes computation (Equations (1) and (2)), which only needs to be done once, largely negligible compared to the overall rendering time.
Compute Fused-Planes
Render 1 frame (4)
Render 30s video
(Eq. 1
@ 30 fps (1)
Tri-Planes
23.30
20.97
Fused-Planes
0.65 ms
10.95 ms
9.85
Table 11: Rendering speed comparison (FPS). We compare the rendering FPS of our method with the baselines during inference. The Nerfstudio implementations are used for all baseline models. Fused- Planess showcases significantly faster rendering speed than all baselines except 3DGS.
FPS (t)
Vanilla-NeRF
0.85
Instant-NGP
48.7
TensoRF
13.6
3DGS
176.0
K-Planes
14.3
Tri-Planes
42,9
Fused-Plaa Paane-
91.3
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D.5 RESULTS USING
VAE
Table 12 reports results where we train Fused-Planes with a VAE that has been reset (all weights are randomly initialized) and trained our scenes with low budget. order avoid backpropagating random gradients to the modules in Fused-Planes at initialization, we allocate 15% of training time warm up the VAE after its reset, using the images of the scenes. This is necessary as training Fused-Planee with non-functional VAE makes it impossible for
to learn the scenes.
This experiment is conducted on 2000 scenes from ShapeNet Cars. The results indicate that employ- ing a VAE trained on smaller dataset and with lower budget introduces only minor degradation in output quality. This suggests that our framework exhibits low sensitivity to the specific initialization of the VAE.
Table 12: Results using a low-budget VAE. Using low-budget VAE with Fused-Plance leads to only minor quality degradation, showing that our mehthod robust VAE initialization.
PSNR
SSIM
LPIPS
Fused-Planes (low-budget VAE)
29.22
0.953
0.035
Ensed-Plance
30.27
0.960
0.033
D.6 TOTAL COST COMPARISON ACROSS DIFFERENT VALUES OF N
For completeness, we report total training time (Table 13) and total memory footprint (Table 14) when varying the number of object N being learned. The results show that Fused-Planes presents competitive training times, and is the fastest planar method. In terms of memory, Fused-Plane and Mused-Plaans-Ulle are the most lightweight methods
Total training time across different values of N. Fused-Planes is the fastest planar method, and present competitive training times compared to other non-planar baselines. All training times are reported in days.
Table 13:
Total training time (days)
Planar
N 1000
2000
N 5000
= 10000
20000
Vanilla-NeRF
X
442.2
884.4
2211.1
4422.2
8844.4
Instant-NGP
X
5.2
10.4
26.1
52.2
104.4
TensoRF
X
47.9
95.7
239.3
478.7
957.4
3DGS
X
6.5
13.0
32.5
65.1
130.1
K-Planes
52.3
104.7
261.6
523.3
1046.5
Tri-Planes
44.7
89.3
223.3
446.7
893.3
Fused-Plance ULW
7.2
12.2
27.1
52.0
101.7
Fused-Planes
8.3
14.5
33.2
64.3
126.5
D.7 MEMORY BREAKDOWN
Table 15 provides a breakdown of the memory footprint of the different components used. The memory cost required by a single object notably low compared to our baselines in the main paper. The memory cost required by shared components can be considered acceptable entry cost, as its value less than a single K-Planes representation.
E BASE PLANES ANALYSIS
To further investigate the learned representations in our base planes, visualize their contents, analyze the values in the weights W,, and interpolate between different weights. We present our analysis below.
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Table 14: Total memory cost across different values of N. Fused-Planes the lightweight method among all baselines. Sizes are reported in GB.
Total memory footprint (GB)
Planar
N
1000
2000
N 5000
10000
20000
Vanilla-NeRI
X
21.5
107.4
214.8
429.7
Instant-NGP
X
184.7
369.4
923.5
1847.0
3694.0
TensoRF
X
203 44
406.9
1017.2
2034.4
4068.7
3DGS
X
27.0
54.0
135.1
270.1
540.2
K-Planes
400.6
801.1
2002.8
4005.6
8011.1
Tri frl-Planes
1.5
2.9
7.3
14.6
29.3
Fused-Planes-UILW
0.4
0.4
0.4
0.4
0.4
Fused-Planes
0.8
1.3
2.7
5.0
9.7
Table 15: Memory breakdown. This table breaks down the memory footprints of the different components in pipeline. Note that the memory usage of shared components remains constant and does not depend on the number of objects. In contrast, the memory footprint for storing objects increases linearly with the number of objects. Therefore, in large-scale settings, the dominant factor the memory that increases with the number objects, as illustrated Figure 4.
Module
Shared?
Size
Encoder Es
130.38 MB
Decoder De
178.86 MB
50x base planes B (Fmace 22) 1x tiny MLP (renderer R,)
V
51.5 MB
V
14.27 KB
1 x miero plane Timlc (Fwic = 10)
X
480 KB
1x weight W;
X
811 B
Memory footprint of shared components
360.75 MB
Memory footprint of a single object
X
0.481 MB
Protocol for base planes visualizations. Recall that, in our standard pipeline, we render learned MuseedPllane-UULW representation Ti (corresponding scene i) using volume rendering followed by a decoder, where each fused representation is defined as:
T.
Σ
W Bx
(12)
where is the ultra-lightweight variant of our method (i.e. no micro planes).
To visualize our base planes, we do not render T. Instead, we directly render individual base planes model trained on ShapeNet Cars and Basel Faces datasets. This
Bg, using our MassedPlaane--USS is indeed possible for our Fused-Planes-ULWW model, each Bş has the same dimensionality T;
Using this protocol, we visualize 10 different base planes Figure As illustrated in the figure, the base planes can be grouped into two categories: (i) semantic: some base planes clearly encode object-level structures (e.g. faces, cars), (ii) residual: other base planes capture finer intra-class variability relative to the semantic base planes. Together, these base planes contribute to each object representation.
Moreover, visualize the values of the weights W, for two different cars. The results presented in Figure 8. We observe that a few base planes dominate the final fused representation, and the dominant planes vary across scenes, while other base planes contribute only minor adjustments.
Finally, we illustrate in Figure 9 the Mnsed-Planes--e Planes-ULWW resulting from a weight interpolation. Specifically, first choose two weights W, and W2 corresponding two scenes. We then compute
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W; tW; (1 22 for {0, 0.25, 0.5, 0.75, 1}. Injecting W, into Equation (12) yields set of Fused-Plame MlasedPlanns--ee Planes-ULW. which we render and visualize.
We observe that interpolating between weights yield coherent scenes, where transition smoothly from one scene to another (c.g. the mouth closes gradually across the different faces).
(a) Cars
(b) Faces
Figure 7: Base planes visualizations. We observe that some base planes clearly encode object-level structures, while other encode finer intra-class variability. Together, these base planes contribute each object representation.
0.05
Weight
0.00
-0.05
-0.10
Index
(a) Weight values.
(b) Corresponding objects.
Figure 8: Learned weights W, for two scenes of our ULW model. The weight W; E IR-M is learned to linearly decompose Fused- T; on the set of base planes { using Equation (12). In this figure, we show the learned weights (left) corresponding to two objects (right). notice that a few base planes dominate the final fused representation, and the dominant planes vary across scenes, while other base planes only contribute to minor adjustments
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Figure 9: Interpolation between base planes weights. We linearly interpolate between the weights of two scenes (leftmost and rightmost). We observe that this interpolation leads to coherent struc- tures, where we transition smoothly from one scene to another.
CODENERF-A
CodeNeRF. CodeNeRF (Jang Agapito, 2021) learns set of scenes with a single neural rep- resentation fo which is conditioned on scene-specific latent codes. Specifically, for each scene, shape code 2, and an appearance code 2, is learned, such that fø(zs; za) models the current scene. Once the conditional NeRF so is trained on large set of scenes, it can learn new scenes using test-time optimization. This test-time optimization consists of learning a new scene by optimizing only the codes (2,, 24), while keeping fe fixed. By reducing the number of trainable parameters, testtiime-opimmizaaion offers increased training speed. Furthermore, the memory required to store an additional scene disk is very low, since only (æ,, =a) need be stored.
CodeNeRF-A. our experiments, we introduce CodeNeRF-A as a new comparative baseline. CodeNeRF-A employs novel training procedure inspired by ours, which leverages the test-time optimization method originally proposed by CodeNeRF to improve efficiency for learning multiple scenes. Specifically, first train the shared neural representation So of CodeNeRF on subset O of O composed of Nı scenes. Subsequently, we employ test- cime-optimization with the previously trained representation learn the remaining scenes O2, with lowered training times.
We present in Table 16 comparison of CodeNeRF-A performances when taking Ni = 500 and Nj 1000. CodeNeRF-A showcases better performances with N₁ 1000, which we set through- out the paper for this method.
Table 16: Choice of N1 for CodeNeRF-A. CodeNeRF-A showcases better performances when taking N1 1000, which we set throughout the paper for this method.
ShapeNet datascts
Bascl Faces
N1
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
CodeNeRF-A CodeNeRF-A
26.81 26.99
0.9108
0.1281
34.15
0.964
0.011
1000
0.9154
0.1257
35.44
0.971
0.010
G HYPERPARAMETERS
For reproducibility purposes, Tables 22 and 23 expose hyperparameter settings respectively for the first and second regimes of our training. A more detailed list our hyperparameters can be found in the configuration files of our open-source code.
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Figure 10: Large-scale results. ShapeNet cars.
We qualitatively show subset of our large-scale results on
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Table 17: Per-object quantitative comparison on Basel Faces.
Face 1
Face
Face 3
Face
PSNR
LPIPS
LPIPS
PSNR
L.PIPS
LPIPS
Vanilla-NRRF
43.44
0.996
0.001
43.90
0.997
0.001
42.65
0.996
0.001
41.64
0.994
0.003
Instant-NCP NGP
37.79
0.987
0.004
40.01
0.990
0.002
35.38
0.977
0.013
32.96
0.969
0.016
TensoRF
40.80
0.993
0.083
42.72
0.995
0.001
40.95
0.993
0.003
38.16
0.982
0.011
3DGS
CodeNeRP
35.49
0.974
0.009
36.35
0.974
0.006
34.42
0.900
0.012
35.00
0,971
0,012
CodeNeRF-A
36.25
0.974
0.008
37.14
0.977
0.005
32.49
0.961
0.015
35.87
0.972
0.013
Planes
40.68
0.993
0.003
39.46
0.988
0,010
41.11
0.993
0.004
39.68
0.990
0.004
Tri-Planes
Fused-Plimes-LL
33.84
0.950
0.013
35.00
0.958
0.007
33.41
0.959
0.011
33.58
0.954
0.010
Planes
36.24
0.970
0.007
38.63
0.975
0.004
37.04
0.975
0.006
37.04
0.971
0.006
Table 18: Per-object quantitative comparison ShapeNet Cars.
Car
Planar
1.PIPS
Vanilla NanFllaNeRRF NeRF
38.43
0.995
0.003
41.20
0.995
0.005
37.43
0.991
0.005
39.53
0.995
0.003
Instat-NGP
35.31
0.986
0.008
37,88
0.990
0,010
34.05
0.987
0.013
36.33
0.989
0.007
0.994
0.005
0.966
0.010
CodeNeRF
27.87
0.950
0.055
28.05
0.937
0.097
26.19
0.929
0.088
27.07
0.930
0.075
CodeNeRF-A
27.10
0.966
0.055
26.86
0.929
0.103
25.25
0.921
0.092
27.10
0.932
0.074
Planes
30.11
0.962
0.024
30.13
0.949
0.043
28.86
0.949
0.040
29.67
0.950
0.039
Fused Planes-U
27.60
0.938
0.054
29.91
0.948
0.064
28.44
0.945
0.050
28.87
0.942
0.051
Fused Planes
30.15
0.964
0.021
31,20
0.961
0.043
29.69
0.958
0.035
30.05
0.954
0.033
Table 19: Per-object quantitative comparison on ShapeNet Sofas.
Planar
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
Nanill-NeRFF
31.06
0.966
0.034
31,83
0.965
0.032
33.58
0.940
0.122
36.82
0.984
0.013
InstantNNii
29.91
33.60
0.027
35.54
0.016
0.987
0.010
0.013
ADGS
30.85
0.986
0.020
33.95
0.989
0.025
34.00
0.982
0.023
33.46
0.984
0.047
CodeNeRF
25.47
0.938
0.113
29.80
0.938
0.068
29.18
0.919
0.139
30.14
0.944
0.100
CodeNoRFAA
0.067
28.05
K-Planes
25.84
0.947
32.28
0.974
0.028
Tri-Planes
26.34
0.929
0.082
29.24
0.930
0.091
28.89
0.903
0.121
29.43
0.922
0.118
Fused- ULW
24.72
0.921
0.130
30.29
0.938
0.047
29.99
0.917
0.091
31.06
0.947
0.069
Fused-Planes
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Table 20: Per-object quantitative comparison on ShapeNet Speakers.
Speaker
Speaker 2
Speaker
Speaker
Planar
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNK
SSIM
LPIPS
Vanill--eRRF NeRF
35.95
0:962
0.065
30:97
0.980
0.021
35.41
0.970
0.032
33.65
0.997
0.015
TensoRF
38.73
0.989
0.012
29.74
0.978
0.024
37.57
0.988
0.017
33,60
0.976
0,016
ADGS
34.45
0.981
0.059
24.21
0.870
0.095
31.70
0.979
0.071
28.11
0.940
0.071
CodeNeRP
29.81
0.894
0.133
24.91
0.331
0.106
28.85
0.925
0.178
28.26
0.935
0.126
CodeNeRF-A
K-Planes
33.80
0.969
0.034
21.84
0.905
0.102
32.33
0.963
0.046
27.19
0.923
0.069
PrinPlanes
29.25
0.911
0.147
23.11
0.903
0.098
29.30
0.914
0.160
26.41
0.907
0.132
Pusst-Plimes-LL.
0.942
0.063
Pusod-Plates
0.046
Table 21: Per-object quantitative comparison on ShapeNet Furnitures.
Furniture
Fumiitre
Furmiture 3
Furniture
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
Instant-NGP
35.12
0.951
0.092
33.64
0.977
0.023
33.91
0.95
0.035
34.49
0.959
0.051
TessoRF
38.03
0.974
0.018
36.25
0.989
0.013
36.22
0.933
0.021
35:23
0.964
0.031
3DGS
34.49
0.991
0.033
31.14
0.969
0.051
33.15
0.975
0.050
33.49
0.998
0.052
CodeNeRF-A A
28.65
0.868
0.156
25.18
0.914
0.148
28.47
0.899
0.234
29.11
0.918
0.146
K-
34.41
0.951
0.028
50.72
0.960
0.044
33.01
0.949
0.045
32.51
0.941
0,555
PrinPlanes
26.88
0.875
27.17
0.180
28.05
0.902
27.58
0.886
0.250
PLW
0.075
Fused Planes
30.19
0.962
0.030
30.54
0.957
0.039
29.81
0.947
0.102
32.34
0.972
0.042
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Face
Face 2
Face 3
Face 4
VenRlla-NeRF
Instant-NGP
TensoRF
3DGS
CodeNeRF
CodeNeRF-A
K-Planes
Tri-Planes
Fused- Plannes
Fused-Planess ULW
Ground Truth
Figure 11: from Basel Faces.
Qualitative comparison. Comparison of NVS quality on test views of four objects
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Car
Car 2
Car 3
Car 4
Vanilla-NeRI
Instant-NGP
TensoRF
3DGS
CodeNeRF
CodeNeRF-A
K-Planes
Tri-Planes
Fused-Planes
Fused-Planes- ULW
Ground Truth
Figure 12: Qualitative comparison. Comparison of NVS quality on test views of four objects from the Cars category of ShapeNet.
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Sofa. 1
Sofa 2
Sofa 3
Sofa.
Vanilla-NeRI
Instant-NGP
TensoRF
3DGS
CodeNeRF
CodeNeRF-A
K-Planes
Tri-Planes
Fused-Planes
Fused-Planes- ULW
Ground Truth
Figure 13: Qualitative comparison. Comparison of NVS quality on test views of four objects from the Sofas category of ShapeNet.
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Speaker
Speaker
Speaker 3
Speaker 4
Vanilla-NeRI
Instant-NGP
TensoRF
3DGS
CodeNeRF
CodeNeRF-A
K-Planes
Tri-Planes
Fused-Planes
Fused-Planes. ULW
Ground Truth
Figure 14: Qualitative comparison. Comparison of NVS quality on test views of four objects from the Speakers category of ShapeNet.
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Furnitare
Furniture 2
Furniture 3
Furniture 4
Vanilla-NeRI
Instant-NGP
TensoRF
3DGS
CodeNeRF
CodeNeRF-A
K-Planes
Tri-Planes
Fused- Planees
Tused-Planes- ULW
Ground Truth
Figure 15: Qualitative comparison. Comparison of NVS quality on test views of four objects from the Furniture category of ShapeNet.
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we aim to improve upon the resource costs
Table 22: PuusedPPlanes regime hyperparameters.
Parameter
Value
General
Number of scenes N
2000
Number of scenes for regime 1 N Pretraining epochs
500
50
Number of epochs N(2)
50
epoch
Fused-Planes
Number of micro feature Funic
Number of macro feature F'aa
22
Number of base plane M
Tri-Planes resolution
64
Loss
A (latent)
A (RCB)
A (an)
0.1
Optimization (warm-up)
Optimizer
Adam
Batch size
512
Learning rate (Micro planes 7: (mmi)))
10
Learning rate (Renderer R.))
10-2
Learning rate (Weights W;)
10-2
Learning rate (Base planes BB)
10-2
Scheduler
Multistep
Decay factor
0.3
Decay milestones
[20 40]
Optimization (training)
Optimizer
Adam
Batch size
32
Learning rate (encoder) Learning rate (decoder)
10
10-4
Learning rate (Miero planes T. 7.(min (mie))
10-4
Learning rate (Renderer R.))
10-4
Learning rate (Weights W.)
10-2
Learning rate (Base planes B&)
10-2
Scheduler
Multistep
Decay factor
0.3
Decay milestones
[20, 40]
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Table 23: Fused-Planes regime 2 hyperparameters.
Parameter
Value
General
Number of scenes N
2000
Number of epochs A²(2) epocch
Number of warm-up epochs N ((W))
30
epoch
Mased-Planess
Number of micro feature Fmic
10
Number of macro feature F maa
Number of base plane M
50
Tri-Planes resolution
64
Loss
(latent)
A (RGB)
Optimization (Warm-up)
Optimizer
Adam
Batch size
32
Learning rate (Micro planes 7/
10-2
Learning rate (Renderer R.,)
10-2
Learning rate (Weights W3) Learning rate (Base planes BB)
10 10-2
10 10-2
Scheduler
Exponential decay
Decay factor
0.941
Optimization (Training)
Optimizer
Adam
Batch size
Learning rate (decoder)
10-4
Learning rate (Micro planes (m(m))))
10 3
Learning rate (Renderer R...)
10-3
Learning rate (Weights Wt)
10
Learning rate (Base planes B₂)
10
Scheduler Decay factor
Exponential decay
0.941
32

---

Published as a conference paper at ICLR 2026
Algorithm Training large set of scenes.
1: Input: O, N1, V, Eg, Dv, Ra, -
N22) A(WU)
(latent), A(RGB) A optimizer
opoch" opoch: opoch
2: Random initialization: Tmic, W, B
3:
4: // First N1 500 objects (regime I)
5: for N(1)
"epoch steps do 6: for ji, shufflo[[1, N1] x VT) do
7:
// Compute Micro-Macro Planes
T(mil), T(mac) +- T(mle) [1], W,B T, 4 ((mic)) a @Tfuue))
8:
9:
10:
// Encode, Render & Decode
11:
2i Cuj Coj+ Olilb
12:
zij t Ex(
13:
Enj (T; Ci.))
14:
Rijiăio & Do (Zi.)) De (Zi.)
// Compute losses
(latent) f || E1.5 11
16:
(RGB) + ||Ir. - Ii.sll 11
17:
((n) + | - fr, 11
18:
Lis + (1atentt) (latent) (nGB) L (RGB)) + (ac) (0e)
19:
4/3
20: I/ Backpropagate
21: T(mic), W,, B, a. (pplimiieersseep(((..))))
22:
end for
23: end for
24:
25: // Remaining objects (regime 2)
26: Eg.frecze()
27: epoch=1
28: for .2(2) Nepoch steps do
29: for (i,j) in shuffle([N] + NJ [1, V[)
do
30:
// Compute Micro-Macro Planes
I (iie) (.mas) T [t], W,B
31:
T. + Timii)) @ TT(mac)
32:
33:
11 Encode, Render & Decode
34:
Bi.j C..j + {Oiil
35:
2n,i Eg(Ii.)
36:
Zns + Ro (Ti,
37:
Tt.j (E..)))
38:
epoch ((WU) apoch then
40:
// Warm-up (latent)
41:
Lis -
T (mie) W. opttiiieer.stpp (latent)
42:
43:
else
Training
45:
L ij
(RGB)
+ | - 21,5
T. ((mei)) W., B, a, 10 +- optimizer.stopp (L [
(RGB)
46:
47:
end if
48:
end for
49:
epoch t epoch +
50: end for
33


Reviews:
[{"review_id": "Cq3qn16TAP", "reviewer": "Reviewer_QUGX", "summary": "This paper proposes Fused-Planes, a shared tri-planar representation that improves the efficiency of training large 3D object collections. Instead of training independent Tri-Planes for each object, the method decomposes each object’s planes into a micro component (object-specific details) and a macro component (a weighted sum of shared base planes capturing class-level structure). The model is trained jointly within a 3D-aware latent space, which further reduces computation. Experiments on ShapeNet and Basel Faces show strong results: up to 7.2× faster training, 3.2× smaller per-object memory, and comparable or better rendering quality than Tri-Planes. An ultra-lightweight variant achieves extreme compression with minor quality loss.", "questions": "1. How sensitive is performance to the number of shared base planes and the micro–macro feature split?\n2. Can a single set of base planes handle multiple classes, perhaps with class-conditioned weights?\n3. What do the shared base planes actually learn? Visualizing or analyzing them could offer useful insight.\n4. How dependent is training on the pretrained latent codec? Could the system work with a smaller or scratch-trained encoder?\n5. Could Fused-Planes be tested on real multi-view datasets or downstream 2D-compatible tasks to demonstrate broader utility?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* **Clear and practical contribution.** The paper tackles a real inefficiency in Tri-Plane training and offers an intuitive solution that effectively shares structure within a class.\n* **Strong empirical gains.** Training speed, memory footprint, and quality all improve substantially. The ULW version demonstrates impressive compression with minimal degradation.\n* **Elegant design.** The micro–macro split is simple yet powerful, allowing reuse of planar architectures while amortizing cost across objects.\n* **Comprehensive evaluation.** The experiments, ablations, and comparisons are thorough and well-presented. The results convincingly support the claims.\n* **Readable and well-organized.** The paper is clear, figures are informative, and the setup is easy to follow.", "weaknesses": "* **Single-class limitation.** The method assumes one class per model. Scaling to diverse datasets would require separate sets of base planes, reducing its flexibility.\n* **Limited analysis of shared bases.** The learned base planes are not explored in depth. It is unclear what structures they capture or how weights vary across instances.\n* **Restricted evaluation scope.** Experiments focus only on novel view synthesis of synthetic datasets. No tests on real or multi-class data, or on downstream tasks that might use the planar outputs.\n* **Dependence on latent space.** The model relies on a pretrained VAE initialization, but the sensitivity to this setup and its cost are not fully studied.\n* **Constant overhead.** While per-object cost drops sharply, the shared components introduce a large fixed memory load, which only becomes efficient at larger scales.", "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": "EVFSo6dvqa", "reviewer": "Reviewer_C24d", "summary": "This paper introduces Fused-Planes, an efficient tri-plane–based method for reconstructing large classes of 3D objects. The approach incorporates a Micro component to capture object-specific features and a Macro component to encapsulate structural similarities shared across the object class. In addition, it leverages a 3D-aware latent space to accelerate both the rendering and training processes of Fused-Planes. Compared with the original Tri-Planes, Fused-Planes achieves 7.2× faster training and 3.2× lower memory consumption while maintaining comparable rendering quality. The authors further propose an ultra-lightweight variant that almost entirely omits the micro component, yielding a remarkable 1875× memory reduction. Experimental results demonstrate that Fused-Planes significantly outperforms existing plane-based methods such as Tri-Planes and K-Planes in terms of both training efficiency and memory scalability for large-scale multi-object reconstruction.", "questions": "As noted in the weaknesses section, the paper should better highlight its capability to scale up to thousands of objects. It should also provide a discussion of relevant references regarding the use of 2D parameterization in 3DGS, and clarify the factors contributing to its lower rendering quality.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper introduces a 3D aware latent space as a form of shared representation in the object reconstruction domain, enabling the model to better capture structural similarities across object classes. According to the ablation study, employing this latent representation rather than directly optimizing in RGB space leads to faster convergence while maintaining comparable rendering quality.\n2. Compared to C3 NeRF, which scales only up to around 20 scenes, the proposed approach remains scalable to thousands of objects, demonstrating superior generalization and efficiency across large datasets.\n3. Under the current task setting, the method achieves significant advantages in training speed and memory efficiency over traditional KPlanes and TriPlanes approaches.", "weaknesses": "1. Although the paper claims that Fused-Planes remains scalable to thousands of objects, I do not observe convincing evidence of this property from the presented experimental results or supplementary videos.\n2. In line 103, the paper states that TensoRF, 3DGS, and Instant-NGP cannot be reshaped into image-like tensors. However, to my knowledge, several recent works in the 3DGS domain, such as Animatable Gaussians, ASH, GaussianAvatar, and Reperformer, have successfully employed 2D UV unwrapping or Morton mapping strategies to project 3D point clouds into 2D grids and then apply image-based CNN architectures to learn the appearance of avatars under novel motions. I believe these 2D parameterization methods should be properly cited and discussed for completeness.\n3. While I understand the limitations of Tri-Planes in representing fine details and handling unbounded scenes, the object-centric datasets used in this paper (e.g., ShapeNet, Basel Faces) appear to be extremely simple in geometry. Despite such simplicity, both Fused-Planes and Fused-Planes-ULW still produce noticeably blurry renderings. This level of visual quality makes it difficult to assess the practical value and applicability of the proposed approach.\n4. To my knowledge, several tri-plane-based methods such as TeTriRF can generate highly detailed and realistic renderings of complex human data with relatively lightweight models. It is therefore unclear why the proposed method fails to achieve comparable quality even on synthetic datasets with simple geometries.\n5. Furthermore, in Fig. 5, where Fused-Planes is compared with other per-scene training methods in terms of size and training time, I believe this comparison is highly unfair", "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": "2l7xZqS4iv", "reviewer": "Reviewer_ymg3", "summary": "This paper presents a resource-efficient tri-planar representation (namely, Fused-Planes) for modelling large collections of 3D objects. The core idea is to decompose each object's representation into object-specific 'micro' planes and shared 'macro' planes (constructed from learned base planes), trained in a 3D-aware latent space. The work addresses a real problem, i.e., the computational cost of training thousands of Tri-Planes, and demonstrates impressive efficiency gains while maintaining quality (7.2× faster training, 3.2× lower memory). The combination of micro-macro decomposition with latent space training is well-motivated, and ablations demonstrate both components are necessary. Experiments and ablations are extensively conducted across multiple baselines and datasets.", "questions": "What happens when you train on the entire ShapeNet dataset, not just per-category? How many base plane sets would be needed? What's the break-even point where efficiency gains disappear?\n\nAt what scale (N objects) does M=50 need to increase? Is there a theoretical or empirical relationship between M, N, and object class diversity?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The major strength of this paper is the significant resource savings (7.2× speed, 3.2× memory reduction vs Tri-Planes). Besides, the ultra-lightweight variant achieves 1875× memory reduction. And it maintains the rendering quality.", "weaknesses": "The method only works within a single object class. For multiple classes with large visual variations, you need multiple instances of Fused-Planes. This significantly limits practical applicability. For diverse datasets, the overhead of multiple base plane sets could negate efficiency gains.\n\nFor open surfaces and unbounded scenes, this method is still limited like other triplane methods.\n\nTable 4, Fused-Planes (Micro)(latent space without macro planes) performs worse than Tri-Planes. This suggests the latent space itself introduces quality degradation, which is only compensated by the macro planes. This is concerning because it means the latent space is not providing a better representation per se. It's just enabling the sharing mechanism.\n\nDuring inference, each Fused-Planes requires computing the weighted sum at inference (Eq. 2). What's the computational cost of this operation compared to directly loading a Tri-Plane? For applications requiring real-time rendering, is this overhead acceptable? What are the runtime/FPS at inference time across different methods?", "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": "IXmixzTURB", "reviewer": "Reviewer_NFsJ", "summary": "This paper proposes Fused-Planes, a tri-planar scene representation for efficiently training large collections of 3D object models. Specifically, each object’s planes are decomposed into an object-specific micro-plane and a class-level macro-plane. The training is conducted in a jointly learned 3D-aware latent space, supervised by both the latent and RGB domains. Experiments on ShapeNet and Basel Faces show that Fused-Planes achieves faster training speed and lower per-object memory than standard Tri-Planes, while maintaining comparable rendering quality.", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper tackles a clear challenge in scaling object-centric Tri-Plane models and introduces both micro- and macro-decomposition to exploit inter-object redundancy.\n\n2. The proposed method uses a base-plane bank and a learned weighting mechanism to provide a compact and interpretable way to model class-level structures without sacrificing the planar format useful for 2D backbones.\n\n3. The performance is impressive. The proposed method significantly reduces the training consumption, including time and space overhead, while still maintaining comparable reconstruction performance.", "weaknesses": "1. The memory of each object excludes the shared network components, including encoder, decoder, and base planes, which may lead to an unfair comparison with baselines. It would be better if the authors could provide a more balanced total-cost comparison across different values of N.\n\n2. The method is class-specific, which means it cannot be generalized to unknown classes.\n\n3. The evaluation is on object-centric, bounded scenes, e.g., ShapeNet categories and Basel Faces. The proposed method struggles with fine details and unbounded scenes, limiting its applicability relative to non-planar methods.", "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)