Neural Compression of 3D Meshes using Sparse Implicit Representation
Authors: Jianqiang Wang
Siyu Ren
Junhui Hou
Venue: ICLR 2026 Poster (2026)
Keywords: compression
3D mesh
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The growing demand for high-quality 3D mesh models has fueled the need for efficient 3D mesh compression techniques. However, existing methods often exhibit suboptimal compression performance due to the inefficient representation of mesh data. To address this issue, we propose a novel neural mesh compression method based on Sparse Implicit Representation (SIR). Specifically, SIR records signed distance field (SDF) values only on regular grids near the surface, enabling high-resolution structured representation of arbitrary geometric data with a significantly lower memory cost, while still supporting precise surface recovery. Building on this representation, we construct a lightweight Sparse Neural Compression (SNC) network to extract compact embedded features from the SIR and encode them into a bitstream. Extensive experiments and ablation studies demonstrate that our method outperforms state-of-the-art mesh and point cloud compression approaches in both compression performance and computational efficiency across a variety of mesh models. The source code is available at https://github.com/yydlmzyz1/SIR-SNC.

Published as a conference paper at ICLR 2026
NEURAL COMPRESSION OF 3D MESHES USING SPARSE IMPLICIT REPRESENTATION
Jianqiang Wang', Ren and Junhui Hou'
Department of Computer Science, City University of Hong Kong wang. - jaeci edu. hk, siyuren2-cmmy e cci tii. edu. hk, jh. houecooueciccooo wer.ueiitt..... edu. .hk
ABSTRACT
The growing demand for high-quality 3D mesh models has fueled the need for efficient 3D mesh compression techniques. However, existing methods often ex- hibit suboptimal compression performance due the inefficient representation of mesh data. To address this issue, we propose a novel neural mesh compres- sion method based on Sparse Implicit Representation (SIR). Specifically, SIR records signed distance field (SDF) values only on regular grids near the sur- face, enabling high-resolution structured representation of arbitrary geometric data with significantly lower memory cost, while still supporting precise sur- face recovery. Building on this representation, we construct a lightweight Sparse Neural Compression (SNC) network to extract compact embedded features from the SIR and encode them into a bitstream. Extensive experiments and ablation studies demonstrate that method outperforms state-of-the-ar mesh and point cloud compression approaches in both compression performance and computa- tional efficiency across variety of mesh models. The source code is available https:/ / thub. com/ yydlmmyzlm/ssi. SNC.
INTRODUCTION
With the rapid advancement of applications such as virtual reality, robotics, and autonomous driving. the demand for more detailed and diverse 3D mesh models is growing rapidly. However, due to in- herent limitations in network bandwidth, the development of efficient and robust 3D mesh compres- sion techniques has become increasingly urgent. Existing mesh compression methods (Maglo et al., 2015), such as Draco (Google, 2025) and Video-based Dynamic Mesh Coding (V-DMC) (MPEG, 2025; Zou et al., 2025), all compress mesh vertices and their connectivity directly, often leading to substantial geometric distortion at low bitrates. Point cloud compression (PCC) techniques (Schwarz et al., 2019) offer alternative to 3D mesh compression according to the transformation between point clouds and meshes, but there are severe distortions during the transformation between these two representation formats due the discontinuous and unstructured nature of point clouds.
The suboptimal performance of existing representative geometry compression methods is primar- ily constrained by the inherently irregular structure of the explicit mesh representations themselves. Recently, widely used implicit fields, such as the signed distance field (SDF), can convert irregular meshes into tensors by sampling uniformly distributed grids in 3D space. However, such a repre- sentation suffers from redundant dense sampling, leading to an explosive growth of data volume as resolution increases. Since only sparse grid cells near the surface carry critical information, while most distant cells are useless, a more efficient representation of the implicit distance field is imper- ative. Motivated by this insight, we propose a Sparse Implicit Representation (SIR) that leverages the strengths of implicit distance fields, which offer continuous geometric precision, and sparse rep- resentations, which provide data efficiency, for compact, accurate mesh surface representation. We further propose lightweight Sparse Neural Compression (SNC) module to efficiently extract the sparse latent features and encode them into the bitstream.
Fig. illustrates the proposed compression pipeline. The original mesh is first converted into sparse SDF tensor. Then, the compression stage, the sparse SDF tensor embedded into a compact
"Corresoonding author. 'Equal contribution.

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Published as a conference paper at ICLR 2026
Bitstream
Entropy Codec
Sample
Encader
Decoder
SNC
SMC
Original Sparse Tensor
Compact
Predicted Sparse Tensor
Decompressed Mesh
Sparse Implicit Representation
Sparse Neural Compression
The pipeline of proposed SIR-based neural mesh coding. first represents irregular 3D meshes into regular Sparse SDF Tensor, new implicit representation, and then an auto-encoder network is utilized to compress these tensors, obtaining Compact Sparse Features of each mesh, which is then converted into bitstreams through entropy codec.
Figure 1:
latent feature with lower resolution through sparse convolutional autoencoder (AE) network. The feature quantized and encoded with entropy into the bitstream. The encoder and decoder of AE correspond to the compression and decompression processes, respectively.
We perform extensive experiments across diverse datasets, demonstrating that the proposed neural mesh coding framework based on SIR and SNC achieves significant improvements over state-of- the-art alternatives, including mesh coding algorithms Draco (Google, 2025) and V-DMC (MPEG, 2025; Zou et al., 2025); PCC methods G-PCC (MPEG, 2025) and SparsePCGC (Wang al., 2023); and another SDF-based approach, NeCGS (Ren et al., 2025). Furthermore, our method exhibits fast encoding and decoding speeds, compact model size of only several hundred kilobytes (KB), and robust performance across different types of meshes.
We summarize main contributions follows.
We propose sparse implicit representation (SIR), enabling structured and accurate representa- tion for 3D meshes, while achieving computationally and memory-effiee -efnorreeffiiientt processing through sparse operations.
We further develop a sparse neural compression (SNC) method leveraging SIR, which demon- strates substantial improvements taining fast encoding/decoding speed and a lightweight model size.
state-of-the-aar mesh compression methods while main-
We offer a fresh perspective to 3D mesh compression and elevate the compression performance to new level, with extensive experiments across diverse mesh models for validation.
RELATED WORK
3D mesh is among the most common 3D geometry representations, yet geometry representation tends beyond this explicit format. In this section, we review representative and recent methods in 3D representation and compression, focusing on the accurate and efficient modeling and compression of geometric surfaces.
Implicit Geometry Representations. Different from widely used explicit representations, such as point clouds and triangle meshes, where the surfaces are represented sampled points or triangles, implicit representations characterize surfaces via isosurfaces of continuous fields. Binary Oceu- pancy Fields (BOFs) (Kazhdan & Hoppe, 2013; Mescheder et al., 2019) and Signed Distance Fields (SDFs) (Park et al., 2019; Atzmon & Lipman, 2020) are two most widely used implicit fields and the surface can be casily extracted from them through Marching Cubes (WE, 1987). However, BOF and SDF divide the whole space into two parts, inside and outside the surface, and they can only represent watertight meshes, limiting their applications to general meshes. Unsigned Distance Fields (UDFs) (Chibane et al., 2020; Ren et al., 2023) can represent more general meshes; however, it is extremely challenging to extract a surface from them.
3D Mesh Compression. Extensive research on mesh compression has been conducted since the 1990s, with comprehensive reviews available in (Peng al., 2005; Maglo al., 2015). Mesh com-
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pression typically involves both vertex coordinates and topological connectivity compression. The widely adopted Draco (Google, 2025) library orders vertices using Morton codes for predictive coding and employs the EdgeBreaker algorithm (Rossignac, 2002) to compress the topological rela- tionships. In the developing MPEG mesh coding standard V-DMC (MPEG, 2025; Zou et al., 2025), the original mesh is decomposed into a hase mesh and set of displacement vectors via subdivision and deformation for separate compression, achieving superior performance. Additionally, some al- ternative approaches convert the mesh 2D geometry images (Gu et al., 2002; Hou al., 201 4a;b; Zhang et al., 2022; 2023; Zeng et al., 2024), where 2D image/video encoders are applied for com- pression. These mesh coding approaches relying on explicit topologiaal representations often suffer from serious quantization distortion and topological breakage in complex geometries, leading to diminishing compression efficiency at higher compression ratios.
3D Point Cloud Compression. An alternative approach to compressing 3D meshes involves first converting them into point clouds and subsequently applying point cloud compression (PCC) tech- niques. PCC encompasses a diverse range of methods leveraging various representations, including 3D voxelization, 2D projected depth images, ID ordered point sequences, etc (Schwarz et al., 2019; Graziosi et al., 2020; Cao et al., 2021). Most PCC methods exploit 3D voxel structures and employ the octree structure to encode occupied voxels. A representative example is G-PCC (MPEG, 2025), standardized by MPEG. Another PCC method, e.g. MPEG V-PCC (Graziosi et al., 2020), utilizes a 3D-to-2DD projection and encodes the projected depth maps, and other data using video codecs. Recently, learning- bauning-based approaches (Gao al., 2025), such as SparsePCGC (Wang et al., 2023), leverage neural networks to compress sparse voxels, have reported superior performance. However, reconstructing meshes from point clouds often introduces severe distortions, which significantly restricts the practical applicability of these methods.
Implicit Field-based Geometry Compression. Implicit representations, such as signed distance functions (SDFs), can convert 3D meshes into structured tensors by uniformly sampling volumetric grids in 3D space, facilitating seamless processing with neural networks. Building on this, several prior works have attempted to compress SDFs. Tang et al. (Tang et al., 2020) employed 3D convolu- tional neural networks to encode TSDF voxel grids. Their method divided the original TSDF volume into non-overlapping occupied blocks of size 8 x 8 x 8 and then independently compressed each block using a small autoencoder network trained in an end-to-end manner. To prevent reconstruc- tion errors, they losslessly compressed the signs of the TSDE. NeCGS (Ren et al., 2025) introduced a learned deformation field on the TSDF volume to improve its capacity to represent fine geometric details. It constructed cluded in the bitstream. However, the dense volumetric representation used in these prior methods inherently confines them low-resolution SDF, limiting their ability accurately capture detailed geometric structures. Although techniques like block partitioning or deformation partially alleviate the problem of representation efficiency, they incur new costs in compression: partitioning limits the use of spatial correlations, while deformation entails extra encoding overhead. These inherent trade-offs ultimately hinder efficient compression.
auto-decoder network that was optimized the entire dataset and in-
PROPOSED METHOD
Given mesh model, our objective is efficiently compress it into compact bitstream while minimizing distortion in the decompressed models. This process consists of two key stages: 1) Ge- ometry Representation and 2) Data Compression. The geometry representation stage transforms unstructured meshes into structured tensors, and the data compression stage encodes this structured data into a compact bitstream, ensuring minimal reconstruction distortion.
To this end, we propose SIR-SNC, a novel geometry compression framework. As shown in Fig. 1, it comprises two key components, a sparse implicit representation (SIR) module and a sparse neural compression (SNC) module. The SIR module efficiently converts unstructured geometry into sparse SDF tensors, from which the original surfaces can be accurately recovered. The SNC module then leverages sparse convolutional neural networks to compress these SIR tensors into the final compact bitstream, achieving high compression efficiency. Next, we will sequentially present these modules.
3.1 SPARSE IMPLICIT REPRESENTATION
Unlike 2D images, which consist of uniformly distributed pixels on regular grids, raw geometry data is inherently unstructured and irregular, posing challenges for direct processing with neural
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networks. Recently, widely used implicit geometry representations, such as SDF, can convert the raw geometry data into regular tensors by sampling on regularly distributed grids in 3D space. However, such a dense representation requires large memory when processed, restricting their practical usage to relatively low resolutions (e.g., 64 or 128 limiting the representation accuracy.
Sparse SDF Tensor. During surface extraction from implicit fields (e.g., SDF) using Marching Cubes (WE, 1987), we notice that we only concentrate on the cubes near the surface while ignor- ing those farther away, shown in Fig. Motivated by this observation, natural solution is to store only the grids near the surface at high resolution, ensuring detailed structural fidelity during surface extraction. Based on this, we propose sparse SDF Tensor. This sparse tensor representation enables efficient and effective representation of 3D meshes, significantly reducing memory usage and facilitating seamless integration with neural networks. At the same resolution, our sparse rep- resentation can achieve the same geometric precision as traditional SDF, while its data volume is significantly reduced by an order of magnitude. From another perspective, our method can be seen as incorporating extra surface distance information alongside discrete point positions, while provid- ing higher precision for continuous surface geometry. Thus, combines the efficiency of explicit discrete points and the accuracy of implicit distance fields.
Given a 3D mesh S, we first sample regularly distributed grids throughout the space and get a set of grids V demee R x x3, where K is the resolution of the grids. We then select the grids whose distance to the surface is less than a predefined threshold r (with a default value of one voxel diagonal length), calculate their corresponding SDF according to the 3D mesh and thereby obtain the sparse SDF tensor to implicitly represent the surface,
V = ((v,,(()))) s(v))lv E Vdense: d(v) <T),
(1)
where d(v) represents the distance from the grid point to the surface, and denotes the SDF value of v. The sign of s(v), indicating inside or outside status, is determined via a ray casting algorithm, i.e., ray from results in -1 for odd intersections and +1 for ones. However, when the resolution K is extremely large, computing V becomes computationally expensive both in time and memory. To address this, we design coarse-to-fine sampling strategy that accelerates the calculation of SDFs for the preserved grids. Further details are provided in the Appendix.
Surface Extraction. To recover the surface from the proposed sparse implicit representation, we leverage an adapted version of the Marching Cubes (WE, 1987), de- noted as Sparse Marching Cubes (SMC). Specifically, for any cube whose eight vertices are present in the Sparse SDF Tensor V, we extract the corresponding triangles following the standard Marching Cubes procedure. Com- pared to the traditional Marching Cubes algorithm, our method is significantly more efficient as it performs trian- gle extraction only in the cubes near the surface.
(a) Watertight
(b) won-watertight
Figure 2: Visual illustration of the sur- face extraction process. The gray cubes are ignored in this process within our sparse representation.
Remark. Since our SIR only focuses on the grids near the surface and explicitly stores their spatial positions, this lo- calized focus enables it to robustly represent more general meshes including non-watertight meshes, without suffering from representation collapse. The cor- responding surface extraction process is illustrated in Fig. 2b. While non-watertight surfaces lack global inside/outside consistency, the signed distances encode meaningful local geometric relation- ships that are sufficient to guide marching cubes in extracting the zero isosurface.
3.2 SPARSE NEURAL COMPRESSION
SIR effectively reduces memory and computation costs for geometry representation. To further reduce storage and transmission costs, we design a Sparse Neural Compression (SNC) module to encode it. As shown in Fig. 3, neural network extracts compact latent features from the sparse SDF tensor via downscaling; these features are then encoded into a bitstream using entropy coding. This entire process is learned end-to-end via a compressive AutoEncoder (AE) architecture.
Learning Latent Representation using Autoencoder. We construct sparse convolutional autoen- coder framework to learn compact latent feature of the original sparse SDF tensor, as shown in
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Sparse SDF
Encoder &
Latent Feature
Decoder
Sparse
Cyy
Fy
DownScaling Block (+2)
Igpate
UpScaling (+2)
Jacz
LyEaE
B Irtrram:
Figure 3: Sparse Neural Compression of SIR. During encoding, the Encoder E downscales the original SDF tensors V into embedded latent features V whose coordinates Cy and attributes Fy: separately encoded into the bitstream. During decoding. the Decoder Da progressively recon- structs occupancy information and sparse SDFs V from V. The Encoder and Decoder networks mainly consist of sparse convolutions (SConv). LMAE, LBCE, and Lute denote the loss functions used in training.
Fig. 3. The encoder processes the input sparse SDF tensor through several downscaling blocks. Each block comprises stacked sparse convolution (SConv) layers with residual connections designed to fully exploit spatial correlations, followed by a voxel downscaling layer that halves spatial resolu- tion via strided sparse convolutions. Through these downscaling blocks, features are progressively extracted and embedded into a lower-resolution sparse tensor, yielding a compact latent feature y that represents the original data with fewer data. The attributes of y are quantized integers and encoded into bitstream via entropy coding, while its coordinates are compressed separately using G-PCC codec (MPEG, 2025).
Correspondingly, the decoder reconstructs the sparse SDF tensor from ) via symmetric upscaling blocks. Each block contains stacked SConv layers and a voxel upscaling layer that doubles the spatial resolution using transposed sparse convolution. To enhance computational efficiency, the decoder will prune redundant voxels after each upscaling operation: An occupancy prediction layer estimates the probability p of voxel occupancy, discarding voxels below a threshold probability. This leverages the inherent sparsity of 3D data to reduce computational overhead. Finally, the sparse tensor is upsampled to the original resolution to output the reconstructed Sparse SDF tensor.
End-to-End Rate-Distortion Optimization. During training, we employ the Mean Absolute Error (MAE) between the reconstructed and original sparse SDF tensors the primary loss function, i.e. IMAE I/V VIIi and use the Binary Cross-Entropy (BCE) between predicted occupancy probabilities and ground truth states as a complement, i.e, LACF. These two losses are combined to supervise reconstruction quality, i.e., Leee LMAE + a LECE,
where is empirically set 0.01. To minimize the bitrate of latent features, we approximate the quantization with additive noise to preserve backpropagation differentiability. and employ factorized entropy model (Ballé al., 2018) to estimate the probability p latent features, from which the bitrate is calculated via the cross-entropy loss. i.e., LRate incorporated as a constraint in the overall loss, i.e.,
Σ, log (p:). LRate is
L LRce + X LRates
where À controls the rate-distortion trade-off.
Remark. The proposed SNC module adopts lightweight network architecture to meet practical complexity constraints, featuring 16-channel convolutional layers with all parameters quantized to 8-bit precision. This design enables deployment with merely around 0.42 MB in storage, while sparse computation optimizations facilitate near-real-time encoding/decoding and resource-efficient training.
Notably, the lightweight and compact network architecture SNC further enables the efficient com- pression of dynamic mesh sequences by leveraging the idea of implicit neural representation (Chen et al., 2021). Specifically, it is feasible to overfit the decoder network parameters of SNC to dy- namic mesh sequence and encode the overfitted network parameters into bitstream. In this way, can implicitly exploit inter-frant frame correlations by the network parameters, avoiding complex inter- frame motion estimation meshes.
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4 EXPERIMENTS
In this section, we validate the effectiveness of our method through extensive experiments and com- parative analyses.
4.1 EXPERIMENTAL SETTINGS
Datasets. Due to the absence of standardized neural mesh compression datasets, we follow (Ren et al., 2025) to construct a multi-source mixed dataset. The training set combines 1,000 meshes each from AMA (Vlasic et al., 2008) (human models), DT4D (Li et al., 2021) (animal models), and Thingi10K (Zhou & Jacobson, 2016) (CAD models), totaling 3,000 samples. For testing, we use: (1) a core Mixed set of 600 meshes (200 per source dataset), (2) 100 diverse CAD models from ShapeNet (Chang et al., 2015), and (3) 1,200 frames across dynamic sequences in the MPEG test set (MPEG, 2023).
Evaluation Metries. Following common practices in 3D reconstruction (Mescheder et al., 2019; Peng et al., 2020), We primarily use Chamfer Distance (CD) as the distortion metric, with F-Score results also reported. Additionally, we report Normal Consistency results in the Appendix. Com- pression bitrate is quantified by bitstream size. The Rate Distortion (RD) curves plot the distortion against the bitrate, with the Bjntegaard Delta Bit Rate (BD-BR) (Bjontegaard, 2001) measuring the relative efficiency. Encoding/decoding times (seconds) assess computational complexity.
Methods under Comparison. In the performance evaluation, we compare with the following rep- resentative 3D geometry compression methods, and more details of the comparison methods are provided in the Appendix.
Mesh Coding: General- purpose Draco (Google, 2025) and the latest V-DMC standard (MPEG, 2025; Zou et al., 2025). V-DMC comparisons are confined to MPEG sequences since its Common Test Conditions (CTC) (MPEG, 2023) only provide configurations for this dataset.
PCC: Standardized G-PCC (MPEG, 2025) and learning- S.PCGC) (Wang et al., 2023). We sample points from the original mesh for compression; af- ter decoding, we reconstruct the mesh via Poisson surface reconstruction (Kazhdan Hoppe, 2013), and remove degenerate vertices distant from the decoded point cloud for rectification.
SparsePGGC (marked as
SDF Compression: The latest work in this field, NeCGS (Ren et al., 2025); we utilize reported test results the Mixed dataset and validate its performance on the ShapeNet and MPEG test sets following its open-source code. A comparison with an earlier work (Tang et al., 2020) is not feasible due to the lack of its open-source implementation and test conditions.
Training and Variable-Rate Inference. In the experiments, the SNC models trained sparse SDF tensors at a base resolution of 256. To obtain models with different rate-distortion (RD) trade- offs, use different À values, e-g., 0.01, 0.005, for the loss term TRate- Each model is optimized with Adam (Kingma & Ba, 2015) with a learning rate of 0.0001 for 50 epochs, and training is completed in one day on single GPU, All training and testing are examined on computer with an Intel Xcon 4309Y CPU and an NVIDIA RTX A6000 GPU.
During inference, variable-rate compression achieved by applying a single trained model to different resolutions. Specifically, we set the resolution of the input sparse SDF tensors as (192, 256, 384, 512) to obtain coarse rate control. Then within the bitrate range of each resolution, fine-grained rate adjustment is achieved by selecting alternative pre- pre-trained models corresponding to different values. The proposed variable-rate method leverages the model's iscolution-annosee property to provide efficient rate adaptation without requiring model retraining. Moreover, it shows potential for achieving scalable coding, providing a direction for future research.
Table 1: BD-BR gains measured using CD for our method against the other methods.
BD-BR Gain
Category
Verts/Faces
G-PCC
S.PCGC
Draco
V-DMC
NeCGS
Mixed
mixed
11k/21k
-55.8%
-74.8%
-57.4%
-39.2%
ShapeNet
objects 82k/162k
-58.0%
-91.0%
-92.0%
-50.8%
MPEG humans
24k/37k
-61.3%
-31.7%
-93.8%
-3055
-46.7%

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Mixed
ShapeNet
MPEG
6.0
Dross G
3.8
5.0
5.5
Ours
N:005
57000
CD (16:3)
5.0
Oode
5.P00C
345315
VOMC
SPOOC
4.5
4.5
3.0
12
15
20
45
15
par frame
Size per frome (KR)
Siz0 per framm (K.B)
(a)
ShopaWNt
MPEC
0.750
0.725
P-Store
0.90
0.700
m0
0.675
0.86
0.630
0.625
0.625
12
20
Size per Irame (KB)
frome
Figure 4: Quantitative comparisons of different methods various test datasets using (a) CD, (b) F-Score as distortion metries. Note that Draco's rate segments on ShapeNet and MPEG are plotted logarithmic scale for clarity, given its much higher bitrate than those of others.
4.2 PERFORMANCE EVALUATION
Dite-Distootion Performance. As shown in Fig. 4, our method achieves significant improvements in compression performance across a wide range bitrates when evaluated on extensive test sets. The BD-BR values in Table 1 further quantify these bitrate savings, specifically, method achieves substantial reductions: 50%-60% compared to G-PCC, 30%-90% compared to SparsePCGC, 50%- 90% compared Draco, 40%-50% compared NeCGS, and 30% compared V-DMC (on the MPEG dataset). These results demonstrate consistent superiority across various test scenarios.
In particular, our method maintains strong performance across all bitrates, while compared methods exhibit specific limitations: PCC methods, i.e., G-PCC and S.PCGC, struggle to achieve low distor- tion the high bitrate range. This limitation stems from their discrete point-based representations, which inherently fail to capture continuous surface details, and thus, surface reconstruction from points is constrained, with unavoidable artifacts arising in the process. Mesh compression methods, Draco and V-DMC, suffer from significant low-bitrate distortion. This is because vertex quanti- zation and topological simplification inevitably result in noticeable geometric deformations, with Draco showing the worst performance due simplistic strategies, Even advanced V-DMC remains constrained by this fundamental limitation. The SDF-based method NeCGS underperforms at high bitrates, mainly due resolution constraints: its traditional dense representation caps resolution 128, even lower than our minimum 192, directly hindering fine geometric detail capture. Beyond this, lacks end-to-end rate-distortion optimization, further limiting compression performance.
Our method demonstrates robust generalization across diverse categories, including objects and hu- mans, as further evidenced by the per-category comparison on the Mixed test dataset (Table This robustness stems from both the mixed train- ing dataset and the concise algorithm design. A key finding is the positive correlation between compres-
Table 2: Per-Category Evaluation on the
Mixed Dataset.
Mixed
AMA
DT4D
Thingi.
Category
humans
animals
3D print
Verts/Faces
10k/10k
18k/36k
3.5k/7k
vs. Draco
-75.7%
-73.2%
-23.9%
vs. G-PCC
-67.0%
-73.6%
-29.1%
sion performance gain and mesh complexity: our method achieves more significant improvements
vs. S.PCGC
-35,2%
-79.2%
-57.5%
gigh-fideiity meshes with abundant vertices and faces (such as those from MPEG and ShapeNet), particularly against Draco. This advantage originates from fundamental divergence in representa- tion. While Draco directly encodes vertices and connectivity, is suitable for simpler models. Our approach more effectively represents and compresses complex 3D spatial structures.

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Ground Truth
Ours
V-DMC
S.PCGC
G-PCC
NeCGS
Draco
Size Distorton(CDD 2.2KB 2299ee33
2.2KB/ 3.28e
2.3KB /2.96e
2.1KB / 3.60e
2.86KB /3.53e-3
13.0KB 6.35e
Ground Truth
Ours
S.PCGC
G-PCC
Draco
Malt
Size/ / Distortion (CD)
36 9KB 6.566 6.56e-3
46.8KB/ 7.02e-3
40.4KB 7.14e-3
6K. 8.888-3
Ground Truth
Ours
S.PCGC
G-PCC
Draco
Size/ Distortion(CD)
13.2KB / 3.21e-3
13.2KB / 3.43e-3
13.0KB /3422e
155.0KB / 7.18B-3
Figure 5: Visual comparisons of different compression methods on the test sets. Zoom in for details.
Visual Comparison. Fig. 5 visualizes the reconstructed meshes across methods; Zooming is rec- ommended for detailed inspection. Our method consistently generates high-quality reconstructions. By contrast, PCC methods, i.e., G-PCC and SparsePCGC, struggle to capture fine surface details even high bitrates. Draco exhibits severe block artifacts, while V-DMC shows significant defor- mation at low bitrates. NeCGS yields blurred details and artifacts owing to its limited resolution and redundant representation. Notably, our method tends produce smoother surfaces with preserved geometric fidelity.
Computational Complexity. We evaluate the computational complexity of various methods by measuring their runtimes on the MPEG test set at medium bitrate points, as summarized in Table 3. For a comprehensive and fair comparison, we account for both the core encoding/decoding time and the pre-encoding conversion, e.g.. SDF calculation, surface reconstruction. All methods are tested on the same platform. Such runtime comparison only gives intuitive reference Ours, NeCGS and SparsePCGC are prototyped using Python and run on GPU, while G-PCC and Draco are implemented using C/C++ and run on CPU.
well the post-decoding conversion, c.g.,
Our method achieves highly competitive processing speeds. The core encoding/decoding times require only around Is. When considering the complete pipeline, the total encoding time is slightly slower than that of Draco, while the total decoding and reconstruction time is only 0. 22 The mesh- to-SDF conversion requires approximately 0.4 as it is currently implemented on the CPU, but is
Table 3: Efficiency analysis of different methods.
Complexity
G-PCC
S.PCGC
Draco
V-DMC
NeCGS
Ours
Pre-Enc.
0.53
0.53
0.40
Time (8)
Enc.
1.64
0.40
0.22
3.42
60
0.10
Dec.
0.26
0.68
0.19
0.42
0.11
0.10
Post-Dec.
5.54
5.22
0.15
0.02
Model Size (MB)
0.77
0.42
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Ground Truth
Ours
S.PCGC
G-PCC
Draco
Size/Distortion (CD)
10.8KB 3.866-3
17.8KB 34.43e
12.6KB 3.622
72KB 6.82e
Size/Distortion (CD)
25.6KB 6..3e-3
26.1KB 7.566-
26.5KB 26.5KB/7.0466 /.04e-
101KB /9.52e-3
Size/iistotiin (CD) 37,2KB / 4.52e-3
46.4KB /4.77e-3
38.2KB 3.75
75.4KB / /9.74e-3
Figure 6: Reconstruction results on non-watertight surfaces. The top two rows are samples from our test set, while the bottom row is ScanNet sample. Zoom in for details.
amenable to GPU acceleration in future work. The surface extraction from SDF adds merely 0.02s. Among the comparison methods, Draco encodes/decodes in around 0.2s. V-DMC suffers from prolonged encoding that exceeds s due to its complex operations. The PCC-based approaches, iee. G-PCC and SparsePCGC, require over seconds for surface reconstruction from points. The SDF-based NeCGS requires over I minute for encoding due to its network optimization procedure. The results underscore the computational efficiency of our approach. Our superior efficiency stems from the compact SIR and the lightweight SNC network, which is only 0.42 MB. This enables our method to be suitable for practical deployment. With further engineering optimizations, especially the migration of pre- pre-processing to GPU, real-time performance can be readily achieved.
Performance on Non-Wer Watertight Surfaces. Our method generalizes well to diverse meshes, in- cluding non- wotttttttt surfaces. As shown in Fig. 6, we select some complex non-watert meshes from the test dataset for validation, and conduct extended evaluations on the commonly used Scan- evaluated the MGN (Bhatnagar al., 2019) open
Net (Dai et al., 2017) datasets. In addition, surface mesh datasets, which are shown in the Appendix. Notably, we used the same compression models as before without fine-tuning them on these datasets. The results confirm that our method effectively captures complex geometric features and outperforms other methods, demonstrating its robust generalization across various meshes. theory, the intrinsic sparsity of our SIR enables a localized geometric representation restricted near the surface and governed by local geometric co- herence. This, in turn, prevents reconstruction collapse that would otherwise depend on having full global topological completeness. However, while the overall reconstruction quality is preserved, minor reconstruction artifacts may appear at the boundaries of open surfaces, which common challenge in surface modeling. Investigating UDFs means of further refinement is promising avenue for future work.
Performance Dynamic Mesh Sequences. We achieve efficient com-
3.6
MPEG
pression of dynamic sequences by implicitly embedding the inter-frame shared characteristics into the overfitted decoder network parameters and
3.5
Ours
Ours (2mmamna)
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m
encoding them into the bitstream. During training, we optimize the net- work parameters on the target dynamic sequence until overfitting. Con- sequently, during compression, the decoder parameters (e.g., weights, bi- ases) are quantized and entropy-coded alongside the per-frame latent fea- tures. Thus, the total bitrate comprises these individual latents and the shared network parameters. The proposed dynamic compression method achieves significant performance gains on the MPEG sequences. As the RD curves in Fig. 7, "Our (dynamic)" achieves around >28% bitrate re- duction compared to "Ours" using feed-forward autoencoder. The de- coding requires around 0.1 per frame due to the lightweight architecture, which aligns with the low-latency requirement of dynamic sequence applications. However, the encoding requires much longer time due to the training process, making it suitable for offline encoding scenarios. It is worth- while investigate accelerating the encoding process in future work.
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Figure 7: R-D compar- ison of dynamic mesh sequence compression.

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Published as a conference paper at ICLR 2026
4.3
ABLATION STUDY
We conducted systematic ablation studies to demonstrate the effectiveness of key choices and their configurations.
Sparsity Threshold. In the proposed SIR, we extract grid points with distance the surface less than threshold T as in Eq. 1, and organize the selected points using a sparse tensor for effi- cient handling. Consequently, determines the point quantity. As depicted in Fig. left, var- ious resolutions, adjusting the threshold leads to a linear change in the number of points. In Fig. 8 right, we examine how influences distortion and note that the distortion reduces to a minimum as the threshold surpasses approximately 0.75 of the voxel diagonal length. For our experiments, we typically set this threshold to 1.0, which offers sufficient buffer to maintain quality while allowing users the flexibility to adjust according their specific needs.
Thres: Points
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Mannaas Bal.
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Figure 8: Ablation study of sparsity threshold.
Selection of Network Hyper-parameters. Our compression architecture is built upon stacked SConv layers to exploit spatial correlations. We primarily investigate the impact of the number of residual network blocks, as summarized Table 4. Increasing the blocks enhances performance by expanding the receptive field; based on balance between performance and complexity, we adopt 5 ResNet blocks. In contrast, increasing the number of feature channels beyond the default 16 (e.g., to 32) significantly raises model complexity without yielding significant performance gains.
Table 4: Ablation studies of residual blocks.
# ResNets
3
7
BDBR(%)
anchor
-18%
-25%
-28%
Params
100K
260K
430K
970K
Contribution of Occupancy Loss. Our distortion loss combines an occupancy loss with the SDF loss (Eq. 2) to predict voxel occupancy after upscaling. This enables pruning of unoccupied voxels, maintaining sparsity throughout the decoding process. Ablation studies confirm its necessity: omit- ting this loss forces the retention of all voxels, which inevitably increases computational cost and introduces fragmented artifacts. In contrast, incorporating the occupancy loss improves reconstruc- tion quality by approximately 47%. The loss weight is set to 0.01 in the experiments; nevertheless, the method is robust, values like 0.1 result in only 2% variation.
CONCLUSION AND FUTURE WORK
We have proposed SIR-SNC, novel framework for high-efficiency 3D mesh compression. SIR em- ploys sparse SDF tensors to accurately represent continuous surfaces with low memory cost, while SNC utilizes a lightweight neural network embed these sparse tensors into lower-resolution latent features, which are further compressed into bitstream via entropy coding. Our method demonstrates superior compression performance, achieving least 30% bitrate reduction compared to state- art mesh compression methods across diverse meshes. Additionally, and it adapts well to complex nat-wttert surfaces and dynamic mesh sequences. Notably, maintains near-real-time efficiency, with both core encoding and decoding completed in around 0.1 s using compact 0.42 MB model, making it highly practical for deployment. Future extensions will continue to investigate efficient compression techniques for high-fidelity and large-volume meshes and incorporate texture compression capabilities.
ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under Grant 62422118 18, and in part by the Hong Kong Research Grants Council under Grants 11219324, 11219422, and NL1ityU1144/25.
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A APPENDIX
A.1 COARSE-TO-FINE CALCULATION OF SPARSE IMPLICIT REPRESENTATION
During the calculation of the Sparse SDF Tensor of a given mesh, we first sample uniformly distributed grids in 3D space and obtain a set of initial grids, Vinit E IR R Kinit X3 where Kinit is the ini- tial resolution. Then we select the initial grids with a distance to the surface of less than a pre-defined threshold Timits V = [v|d(v) < Tinit), Based on V, we can obtain grids of higher resolution through upsampling, as illustrated in Fig. 9, from which we can select the grids near the surface until we achieve the required resolution. Such a sampling strategy can accelerate the calculation of Sparse SDF Tensors.
Initaal Grtds
Terpd Grids
Upsampling Pruziay
Upuzzping Presiag
Figure Coarse-to-fine Calculation of SIR.
A.2 EFFICIENCY COMPARISON OF DIFFERENT 3D REPRESENTATIONS
The memory and computational efficiency of 3D representation are primarily determined by its sparsity, which is quantified by the number of points voxels required. We quantitatively compare this efficiency across resolutions for various representations cally with resolution, incurring substantial costs. A strategy mitigate this is to partition the volume into K occupied blocks (Tang et al., 2020) for independent processing. However, small block sizes (e.g., k=8) disrupt inter-block continuity and hinder the exploitation spatial correlations vital for compression. In contrast, TSDF-Def in NeCGS(Ren et al., 2025) applies an extra deformation field the TSDF volume to improve representation accuracy; however, this deformation relies on itera- tive optimization and is computationally expensive. The sparse point cloud is efficient; however, it struggles to represent surfaces with high accuracy.
Fig. 10a. TSDF volumes scale cubi-
Our sparse implicit representation combines the strengths of both TSDF and point cloud. It ex- plicitly samples points near the surface, organizing them into memory-efficies sparse tensors via [N, 3] coordinates, ensuring inherent sparsity and continuity. Fig. 10a shows our method requires significantly fewer points than a dense TSDF and about 4.3 times fewer than an 8 blocked TSDF. Notably, this sparsity does not compromise accuracy. Compared to point clouds (Figs. 10a and 10b), our method uses only about 2.3 times as many points yet achieves a significant reduction in distortion. This optimal trade-off, summarized in Fig. 10c, underscores the superior efficiency of our representation, which effectively bridges the accuracy of TSDF with the sparsity of point clouds.
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Figure 10: Comparison of efficiency and distortion across resolutions for different representations.
Ground Truth
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Figure 11: Visual comparison under different resolutions of SIR.
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A.3 ANALYSIS OF RECEPTIVE FIELD IMPACT ON COMPRESSION PERFORMANCE
We investigate how the receptive field influences compression perfor- mance. As indicated by the RD curve in Fig. 12, increasing the depth of the residual network to enlarge the receptive field (for instance, us- ing a 5-resnet-block architecture instead of single resnet block leads to a 25% improvement. While dividing the data into independent 8x8x8 blocks causes a performance drop of more than 80% even after retraining on the partition data. These results show that large re- ceptive field is essential for effectively modeling spatial correlations during compression. By comparison, block-based compression re- stricts the receptive field to small, isolated regions, which severely limits achievable performance.
ResNee
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RecSee Pystisn
8
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Figure 12: Ablation study of network receptive filed.
A.4 DETAILS OF COMPARISON METHODS
For fair comparison, we used the open-source code of other methods and adhered their respective test conditions. For PCC algorithms,, we densely sampled point clouds from mesh surfaces, voxelized them to 10 bits for compression, and employed Pois- son surface reconstruction with post-processing to reconstruct meshes from point clouds.
Shapchet
MPPC
24
EURS
24
5ion per from (8ln
SoF per froms (Kin
Figure 13: Comparisons with G-PCC in trisoup mode.
The configuration and bitrate settings of each method as follows: For Draco, adjust QP from 6 to 11 obtain different bitrates. For V-DMC, we use all-intra mode, set QP from 22 to 44 and adjust "target" parameters to obtain op- timal bitrates following its common test conditions. For G-PCC, used octree mode and adjust position LuniioonnnnnizaionSaaleee from 0.5 to O. 15 to obtain different bitrates. Comparisons with G-PCC its open-source pretrained models
in trisoup mode are provided in Fig. 13. For SparsePCGC, to generate various bitrates. For NeCGS, we use its reported results on the Mixed dataset and test its performance on other test following its open-source code.
A.5 EVALUATION USING NORMAL CONSISTENCY
In addition to the Chamfer Distance and F-Score metrics, we also report Normal Consistency results following common practice, as shown Fig. 14. This further demonstrates the performance of our method.
Mitee
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Figure 14: Quantitative comparisons of different methods on various dataset using Normal Consis- tency.
A.6 MORE VISUAL RESULTS
Fig. 15 presents additional visual comparisons on the test dataset. It evident that the reconstructed models generated by our method exhibit less distortion compared to those from the baseline meth- ods. Fig. 16 shows results of meshes from the MGN dataset, demonstrating the effectiveness of our method in compressing open-surface meshes. Fig. 17 showcases the results of meshes with complex structures, illustrating that our method can effectively preserve detailed structures.
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Ground Trush
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Figure 15: Visual comparisons of different compression methods. From top to bottom, the examples correspond to those from AMA, DT4D, and ShapeNet.
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Figure 16: Visualization of decompressed results for non-watertight meshes from MGN dataset.
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Figure 17: Visualization of decompressed results for meshes with detailed structures.
15


Reviews:
[{"review_id": "9XPqLOEslo", "reviewer": "Reviewer_EZUy", "summary": "This paper proposes a framework for 3D mesh compression called SIR-SNC.The paper introduces a two-stage approach, 1) Sparse Implicit Representation (SIR): A mesh representation that stores Signed Distance Field (SDF) values only on a regular grid of points near the object's surface, 2) Sparse Neural Compression (SNC): A lightweight, sparse convolutional autoencoder (AE) designed to compress the SIR. A Sparse Marching Cubes (SMC) algorithm is used for efficient surface extraction from the SIR.", "questions": "Please elaborate extensively on the differences from the method of Tang et al. and exactly what aspects of the proposed method are claimed to be novel and validated via the experiments.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Promising results on the evaluated benchmarks. The paper shows large gains on most of the benchmarks.\n- Efficiency of method: The proposed method appears to be quite practical using a small model and fast encoding/decoding.\n- The method works on general non-watertight and open meshes.", "weaknesses": "- Limited Novelty: The proposed approach appears to closely follow that of Tang et al., without sufficiently acknowledging the fact and without presenting relevant empirical comparison. It is not clear what aspects of the proposed approach are truly original. More on this in the next point.\n- Insufficient comparison with Tang et al.: The method seems to be heavily inspired by that of Tang et al (cited in the paper). As such, it is not clear what aspects of the paper are novel and what are borrowed from existing works. For example, Tang et al, also uses a sparse representation, using an occupancy map (which is losslessly coded and transmitted) to encode the occupied voxels. These occupied voxels are also found using marching cubes algorithm (like the current paper). This allows Tang et al. to avoid coding and transmitting voxels that don’t carry useful information (Fig 3 in Tang et al.). This fact is not mentioned in the paper and/or attributed to Tang et al, though the figures of Sparse SDF in the paper (e.g. Figure 4, first and last diagram) are reminiscent of Figure 3 in Tang et al. Similarly, Tang et al, uses a small autoencoder to encode/decode features of only the occupied blocks of voxels. Again, this is not acknowledged/mentioned in the paper. Further, Tang et al, also uses a rate-distortion loss (eq 1 in Tang et al) to train a compressed representation (similar to eq. 3 in current paper).\n\nOverall, I find it quite concerning that sufficient credit/comparison is to made with the prior art of Tang et al, despite the obvious similarities in the proposed method. While the presented results are promising, I am quite curious about the source of this omission.", "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": "n3ir4XtJ3O", "reviewer": "Reviewer_YpR1", "summary": "This paper introduces a combination of Sparse Implicit Representation (SIR) and Sparse Neural Compression (SNC) through a two-stage approach to achieve significant file size reduction while maintaining reconstruction precision. The key insight is that by storing explicit coordinates with distance values for near-surface points only, SIR achieves high-fidelity representation with reduced file sizes compared to established methods. Results demonstrate impressive compression gains over prior methods across multiple datasets, with effective generalization to unseen meshes, support for non-watertight surfaces, and practical efficiency through sub-second decoding times.", "questions": "1. Can you provide systematic ablation studies on key design choices?\n2. What is the theoretical basis for non-watertight surface handling?\n3. Can you clarify the source of computational efficiency gains?\n4. How does your method generalize across different mesh types and resolutions? \n    - Can you provide per-category performance breakdowns showing where the method excels versus struggles?\n    - What happens if the resolution exceeds $512^3$ significantly?\n    - Are there mesh characteristics (topology, geometric complexity, scale) that predict compression performance?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: The paper demonstrates strong originality through simple but powerful insight that only near-surface voxels matter for reconstruction, thus introducing a novel two-stage framework that stores explicit coordinates with distance values exclusively for near-surface points that achieves better compression (small resultant file size) and performance (shorter decoding time).\n\nQuality: Sufficient comparative evidence were provided to support the authors' claims, validated across multiple datasets and benchmarked against multiple established methods. Furthermore, their cross-domain generalization testing provides convincing evidence that the method generalizes beyond its training distribution.\n\nClarity: The paper effectively communicated why sparse representation addresses inefficiencies in dense approaches. The primary contributions and insights are easy to understand and follow.\n\nSignificance: Substantial compression gains (30-90% bitrate reduction over state-of-the-art) across multiple datasets represents genuine practical value. The ability to handle non-watertight surfaces and performance gain could warrant additional development to transition this into a viable alternative for existing methods.", "weaknesses": "The absence of systematic ablation studies represents a critical methodological gap. The paper makes numerous design choices e.g., sparsity threshold, channel network width, etc., described as \"empirically set\" or \"default\" without justification. We cannot determine which components drive performance or whether results are robust across hyperparameter variations. At minimum, the authors should ablate the sparsity threshold, which dictates the rate-distortion tradeoff, network architecture choices, and loss component contributions. Without these studies, the work reads as \"we tried something and it worked\" rather than \"we understand why our design succeeds.\"\n\nTable 2 mixes heterogeneous implementations of the work (Python/GPU) and compares it against optimized C++/CPU implementations, making it hard to truly discern if the  observed speed differences originated from algorithmic insight or implementation choices.\n\nThe non-watertight surface handling lacks sufficient justification. The paper claims the method \"robustly represents\" non-watertight meshes based solely on showing it works in practice, without explaining why it should work reliably. While the intuition makes sense, missing the explanations of when and why the approach succeeds or fails, measurements of whether geometric properties are preserved correctly, and comparisons with methods specifically built for handling non-watertight surfaces weakens the paper. A deeper analysis showing when it works, when it doesn't, and how well it preserves geometric structure is valuable here.", "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": "k5t7a8xn8L", "reviewer": "Reviewer_b2HU", "summary": "This paper presents a novel neural mesh compression framework named SIR-SNC, which addresses the inefficiencies of existing 3D mesh compression methods. It introduces: 1) Sparse Implicit Representation (SIR): Efficiently encodes surface geometry by storing SDF values only near the surface; 2)Sparse Neural Compression (SNC): A lightweight autoencoder based on sparse convolutions that compresses the SIR representation into compact latent features and encodes them into a bitstream. Experiments on multiple datasets show that SIR-SNC significantly outperforms prior methods, including Draco, G-PCC, SparsePCGC, NeCGS, and V-DMC, both in rate-distortion performance and computational efficiency. Notably, it generalizes well to non-watertight and dynamic mesh scenarios.", "questions": "1. What are the relations between the initial grids and the target grids?\n2. How sensitive is SIR to the threshold \\tau?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1)\tTechnical novelty: The SIR design is a well-motivated innovation that combines the best of sparse geometry and implicit fields.\n2)\tStrong empirical performance and efficiency: The proposed method achieves significant BD-BR reductions across diverse benchmarks, while maintaining a lightweight model size and fast decoding speed, demonstrating both superior compression effectiveness and practical efficiency.\n3)\tRobust generalization: The method generalizes well to diverse and challenging meshes without any fine-tuning. It was validated on unseen datasets such as ShapeNet , ScanNet, and MGN, including non-watertight and open-surface cases, demonstrating strong robustness across domains and geometry types.", "weaknesses": "There is no ablation study on the predefined distance threshold.", "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)