Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling
Authors: Zhihao Li
Yufei Wang
Heliang Zheng
Yihao Luo
Bihan Wen
Venue: NeurIPS 2025 poster ()
Keywords: 3D Reconstruction
3D Generation
Primary area: applications

Abstract: High-fidelity 3D object synthesis remains significantly more challenging than 2D image generation due to the unstructured nature of mesh data and the cubic complexity of dense volumetric grids. Existing two-stage pipelines—compressing meshes with a VAE (using either 2D or 3D supervision), followed by latent diffusion sampling—often suffer from severe detail loss caused by inefficient representations and modality mismatches introduced in VAE.
We introduce Sparc3D, a unified framework that combines a sparse deformable marching cubes representation Sparcubes with a novel encoder Sparconv-VAE. Sparcubes converts raw meshes into high-resolution ($1024^3$) surfaces with arbitrary topology by scattering signed distance and deformation fields onto a sparse cube, allowing differentiable optimization.
Sparconv-VAE is the first modality-consistent variational autoencoder built entirely upon sparse convolutional networks, enabling efficient and near-lossless 3D reconstruction suitable for high-resolution generative modeling through latent diffusion. Sparc3D achieves state-of-the-art reconstruction fidelity on challenging inputs, including open surfaces, disconnected components, and intricate geometry. It preserves fine-grained shape details, reduces training and inference cost, and integrates naturally with latent diffusion models for scalable, high-resolution 3D generation.

Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling
Zhihao Li Yufei Wang',
Heliang Zheng Yihao Luo 2.3 Bihan Wen l.f
Department of EEE, Nanyang Technological University, Singapore
Math Magic $Imperial-X, Imperial College London, UK
zhihao005ce ntu. edu. sg, yufei001@e ntu.
attu.edu.st edu.sg, zhenghll.@gmail.cmm y. 1uoo2@imeerial..comm ac. uk, bihan... wen edu. sg
https://lizhihen github. io/ //Sparc3D
Figure 1: Sparc3D Reconstruction Results. Leveraging our sparse deformable marching cubes (Sparcubes) representation and sparse convolutional VAE (Sparconv-VAE) our method achieves state-of-the-art reconstruction quality on challenging 3D inputs. It robustly handles open surfaces (automatically closed into watertight meshes), recovers hidden interior structures, and faithfully reconstructs highly complex geometries (see zoom-in views, top to bottom). All outputs are fully watertight and 3D-printable, demonstrating the potential our framework for high- 3D mesh generation. Best viewed with zoom-in.
Abstract
High-fidelity 3D object synthesis remains significantly more challenging than 2D image generation due to the unstructured nature of mesh data and the cubic complex- ity of dense volumetric grids. Existing two-stage with a VAE (using either 2D or 3D supervision), followed by latent diffusion sam- pling ofnggnftenn suffer from severe detail loss caused by inefficient representations and modality mismatches introduced in VAE. We introduce Sparc3D, unified frame- work that combines sparse deformable marching cubes representation Sparcubes
compressing meshes
* 'This was conducted during Zhihao Li's research internship at Math Magic. Corresponndig authors; *:project lead.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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with novel encoder Spareonv- VAE. Sparcubes converts raw meshes into high- resolution (10249) surfaces with arbitrary topology by scattering signed distance and deformation fields onto sparse cube, allowing differentiable optimization. Sparconv-VAF is the first modality-consistern variational autoencoder built en- tirely upon sparse convolutional networks, enabling efficient and near-lossless 3D reconstruction suitable for high-resolution generative modeling through latent diffusion. Spare3D achieves state-of-the-arr reconstruction fidelity challenging inputs, including open surfaces, disconnected components, and intricate geometry. It preserves fine-grained shape details, reduces training and inference cost, and integrates naturally with latent diffusion models for scalable, high-resolution 3D generation.
Introduction
Recent breakthroughs in 3D object generation [2. 15, 17, 24, 29, 32, 39] have enabled applications in virtual domains, such as AR/VR [3, 14, 16, 20] and robotics simulation [34, 35] as well as in physical contexts like 3D printing [29]. Despite this progress, synthesizing high-fidelity 3D assets remains far more challenging than generating 2D imagery text stemming from the inherently unstructured nature of 3D data and the cubic scaling of dense volumetric representations.
Drawing on the success of text-to-image diffusion models [25], many 3D generation pipelines [2. 15, 17. 32, 39] employ two-stage process: a variational autoencoder (VAE) followed by latent diffusion. Most current VAEs employ either 3D supervision[2. 15, 17, 39]-typically via global vector set representation- or 2D supervision(32] commonly via a sparse representation but both suffer from limited resolution and a modality mismatch between inputs and outputs.
3D supervised VAEs [2, 15, 17, 39] require watertight meshes for sampling supervision signals, yet most raw meshes are not watertight and must be remeshed. The common pipeline [2, 15, 39] shown in Fig. 2, first samples Unsigned Distance Function (UDF) on a voxel grid, then approximates Signed Distance Function (SDF) by subtracting two voxel preeshhalving the effective resolution and introducing errors that propagate through both VAE reconstruction and diffusion generation. After applying Marching Cubes [19] or Dual Marching Cubes [26], the mesh becomes double-layered, necessitating an additional step to retain only the largest connected component. This step inadvertently discards smaller yet crucial features, misaligning the conditioned input image from raw mesh with the reconstructed 3D shape during diffusion training.
Most recently, TRELLIS [32] demonstrated the possibility of training a 3D VAE using only 2D supervision. While avoids degradation from mesh conversion, it still depends on dense volumetric grids for 2D projections, which limit resolution. Moreover, lacking any 3D topological constraints, the generated object's interior geometry may be incorrect and its surfaces remain open-a a critical flaw for applications such as 3D printing.
All of these VAEs also contend with fundamental modality gap: 3D supervised methods [2, 15, 17, 39] ingest surface points and normals but decode SDF values, while TRELLIS [32] encodes voxelized DINOv2 [23] features into SDF Bridging this gap requires heavy attention mechanisms, which increase model complexity and risk of amplifying underlying inconsistencies.
In this work, we introduce Sparcubes (Sparse Deformable Marching Cubes), fast, near-lossless pipeline for converting raw meshes into watertight surfaces. Our method begins by identifying a sparse set of activated voxels from the input mesh and performing flood-fill to assign coarse signed labels. We then optimize grid-vent grid-vertex deformations via gradient descent and refine them using a view-dependent 2D rendering loss. Sparcubes converts raw mesh into watertight 10243 grid in under 30 seconds -chheevnn a threefold speedup over prior watertight conversion methods [2, 15] without sacrificing fine details small components.
Building on Spareubes, we introduce Sparconv-VAE. modality-consisten consistent VAE composed of sparse encoder and self-pruning decoder. By eliminating the modality gap, Sparconv-VAE can employ a lightweight architecture without relying on heavy global attention mechanisms. Experi- mental results show that our VAE achieves state-of-the-art reconstruction performance and minimal training cost. Furthermore, seamlessly integrates with existing latent diffusion pipelines such as TRELLIS [32], further enhancing the resolution of the generated 3D objects.

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Raw mesh
UDF
SDF
Double-laee meil
Reconstructed mesk
X wateniight
Sample UNF
LDF t SDF
Marching
Cabes
component
Keep max
wateriihtt
TEW
SOCOECT
double-
missing
Figure 2: Problems of the previous SDFs extraction pipeline. The widely used SDFs extraction workflow [2, 15, 39] suffers from two critical failures: resolution degradation (show as error) and missing geometry (circled effectively halves the spatial resolution. Moreover, the SDF extraction yields a double-layer mesh, from which only the largest connected component is retained, inadvertently discarding smaller but important component. Together, these two deficiencies substantially limit the upper-bound performance of downstream VAEs and generation models. Best viewed with zoom-in.
the right). Converting UDF to SDF by subtracting two voxel sizes
Our main contributions can be summarized as:
We propose Sparcubes, fast, near-lossless remeshing algorithm that converts raw meshes into watertight surfaces at 1024 resolution approximately 30s, achieving 3x speedup over prior methods without sacrificing any components.
We introduce Sparconv-VALE a modality-consissent consistent variational autoencoder that employs sparse convolutional encoder and self- self-pruning decoder. By eliminating the ouput- modality gap, our architecture achieves high computational efficiency and near-lossless reconstruction, without global attention.
Experimental results show that, our Sparc3D framework, comprising Sparcubes and Sparconv- -VF,, achieves state-of-the-ard reconstruction fidelity, reduces training cost, and seamlessly integrates with current latent diffusion frameworks to enhance the resolution of generated 3D objects.
2 Related Work
2.1 3D Shape Representation and Generation
Mesh and point cloud. Triangle meshes and point clouds are the most common representations of 3D data. Triangle meshes, composed of vertices and triangular faces, offer precise modeling of surface detail and arbitrary topology. However, their irregular graph structure complicates learning, requiring neural networks to handle mon-uniform neighborhoods, varying vertex counts, and the absence of canonical ordering. To address this, recent methods [4, 5, 11, 28, 30] adopt autoregressive models to jointly generate geometry and connectivity, though these suffer from limited context length and slow sampling. contrast, point clouds represent surfaces as unordered sets of 3D points, making them easy to sample from distributions [21, 31, 33), but difficult convert directly into watertight surfaces due to the lack explicit connectivity [9, 36].
Isosurface. Isosurface is continuous surface representing a mesh boundary via signed distance field (SDF). Most methods [13, 19, 26, 27] subdivide space into voxels, polygonize each cell, then stitch them into mesh. Marching Cubes uses fixed lookup table but can suffer topological ambiguities [19]. Dual Marching Cubes (DMC) fixes this by placing vertices on edges where the isosurface crosses and linking them via Dual Contouring, yielding watertight meshes [13,26] 26]. Both rely on a uniform cube size, limiting detail; FlexiCubes [27] deforms the grid and applies isosurface slope weights to adapt voxel sizes to local geometry, improving accuracy.
Many 3D generation methods adopt based supervision [2, 15, 17, 24, 32, 38, 39]. Techniques relying solely on 2D supervision [32] may implicitly learn an SDF, but often produce open surfaces or incorrect interiors due to lack of volumetric constraints. In contrast, fully 3D-supervised approaches [2, 15, 17, 24, 38, 39] extract explicit SDFs from meshes with arbitrary topology, mak- ing accurate and adaptive SDF extraction a critical but challenging component for high-fidelity reconstruction.

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2.2 3D Shape VAEs
VecSet-based VAEs. VecSet-based methods represent 3D shapes as sets of global latent vectors constructed from local surface features. 3DShape2 VecSet [37] embeds sampled points and normals into a VecSet using a transformer and supervises decoding via surrounding SDF values. CLAY [38] scales the architecture to larger datasets and model sizes, while TripoSG [18] enhances expressiveness through coxture-off-epertt (MoE) modules. Dora [2] and Hunyuan2 [39] improve sampling by prioritizing high-curvature regions. Despite these advances, all approaches face modality mismatch: local point features are compressed into global latent vectors and decoded back to local fields, forcing the VAE perform both feature abstraction and modality conversion, which increases reliance on attention and model complexity.
Sparse voxel-based VAEs. In contrast, sparse voxel-based VAEs preserve spatial structure by converting meshes into sparse voxel grids with feature vectors. XCube [24] replaces the global VecSet in 3DShape2VecSSe VecSet [37] with voxel-aligned SDF and normal features, improving detail preservation. TRELLIS [32] enriches this representation with aggregated DINOv2 [23] features, enabling joint modeling of 3D geometry and texture. TripoSF [12] further scales the framework to nigh-resolution reconstructions (see Supplementary Material for details). Nonetheless, these methods still face the challenge of modality conversion-mapplnn point normals or DINOv2 descriptors to continuous
SDF fields remains key bottleneck.
3 Method
3.1 Preliminaries
Distance fields. A distance field a scalar function R² - R that measures the distance to a surface. The unsigned distance function (UDF) encodes only magnitude, while the signed distance function (SDF) adds sign to distinguish interior and exterior:
UDF(x, M) = min ||x
yll2, SDF(x. = sign M) UDF(x,M),
(1)
YEM
where sign(x, M) +11 indicates inside/outside status. For non-watertigh non-manifold meshes, computing sign is non-trivial [1].
Marching cubes and sparse variants. The marching cubes algorithm [19] extracts an isosurface mesh from a volumetric field & by interpolating surface positions across a voxel grid:
S E R
(x = 0}.
(2)
Sparse variants [10] operate narrow bands (xx) introduce sparse cube grid (V, C), where is set of sampled vertices and C' contains 8-vertex cubes. Deformable and weighted dual variants, exemplified by FlexiCubes [27], extend this process by modeling the surface as deformed version of the sparse cube grid. Specifically, each grid node n, in the initial grid is displaced to that better conforms to the implicit surface, where the displacements An, and per-node weights w; E W are learnable during optimization.
to reduce memory usage. We further
position n, An, forming refined grid (N + AN, C, Φ W)
3.2 Sparcubes
Our method, Sparcubes (Sparse Deformable Marching Cubes), reconstructs watertight and geometri- cally accurate surfaces from arbitrary input meshes through sparse volumetric sampling, coarse-to-fing sign estimation, and deformable refinement. Unlike dense voxel methods, Sparcubes represents geometry using a sparse set of voxel cubes, where each cube vertex carries a signed distance value. This representation enables efficient computation, memory scalability, and supports downstream surface extraction or direct use in learning based pipelines.
As shown in Fig. 3, the core pipeline consists of the following steps:
Step I: Active voxel extraction and UDF computation. We begin by identifying a sparse set of active voxels within narrow band around the input surface. These are voxels whose corner vertices

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wateriilt
waterggt Receestraciod meih
O
nowmnch
Spariiles resdering
Stepl: Active voucl and UDF
Step2: Flood fill and SDF
Step3: Optimise deformatiio
Stapé; Rendering refinenient
Figure 3: Illustration of our Sparcubes reconstruction pipeline for converting raw mesh into watertight mesh.
lie within a threshold distance from the mesh M. For each corner vertex x E R.S, we compute the unsigned distance the surface:
UDF(x) = min |x - y/122-
(3)
yEM
This yields sparse volumetric grid Φ with distance values concentrated near the surface geometry, suitable for efficient storage and processing.
Step 2: Flood fill for coarse sign labeling. To convert the unsigned field into signed distance function (SDF), we apply volumetric flood fill algorithm [22] starting from known exterior regions (e.g., corners of the bounding box). This produces binary occupancy label T(x) {0, 1), indicating whether point x is inside or outside the shape. We then construct the coarse signed distance field as:
SDF(x) (1 (x)) UDF(x),
(4)
which gives a consistent sign assignment under simple labeling and forms the basis for further refinement.
Step 3: Gradient-baset deformation optimization. Instead of explicitly refining a globally accurate SDF, we directly optimize the geometry of the sparse cube structure to better conform to the underlying surface. Given an initial volumetric representation (V, C, - V denotes the set of sparse cube corner vertices, C is the set of active cubes, and + is the signed distance field defined at each vertex-we perform a geometric deformation to obtain (V + AV, C, Q.).. This results in geometry-aware sparse SDF volume that more accurately approximates the zero level set of the implicit surface. Notably, for points where ((x) > O, the SDF values are often only coarse approximations, particularly in regions far from the observed surface or near topological ambiguities. These regions may exhibit significant errors due to poor connectivity, occlusions, or non watertgtt input geometry. As such, rather than refining & globally, to implicitly correct the spatial alignment of the zero level set. To improve the accuracy of sign estimation and geometric alignment, displace each vertex slightly along the unsigned distance field gradient:
optimize the vertex positions AV V
x 17 VUDF(x).
This heuristic captures local curvature and topological cues that are not easily recovered through purely topological methods such as flood fill. It also allows us to estimate sign information in regions with ambiguous connectivity, such thin shells or open surfaces. The final data structure is a sparse cube grid with SDF values on each corner (V, C, L. AV), denoted as Sparcubes.
Step 4: Rendering- based refinement. Sparcubes supports differentiable mesh extraction, enabling further end-to-end refinement with perceptual signals. When multi-view images, silhouettes, or depth maps are available, optionally introduce a differentiable rendering loss to further enhance visual realism and geometric alignment. Given a reconstructed mesh M, extracted from the deformed Sparcubes, compute multi-term rendering loss:
(6) where RD(M,, denotes the rendered depth image of the mesh under known camera parameters. and R.N (M,) is the corresponding rendered normal map. The terms TDD and T.N. are the observed depth image and the ground truth normal map derived from the input or canonical mesh. Leveraging our voxel-based data structure, we can easily identify visible voxels and render exclusively within those regions, greatly reducing computational cost.
Lrander [[,,(M,,) - II
2 + [I IIN
5

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3.3 Sparconv-VALe VAE
Building on our Sparcubes representation, we develop Sparconv- VAE, a sparse convolution- variational autoencoder without high-com high-consuming global attentions, which directly compresses the Sparcubes parameters { E .. 6 E AV3 into a sparse latent feature and decodes back to the same format without any modality conversion.
Architecture and Loss Function. Our encoder is cascade of sparse residual convolutional blocks that progressively downsample the input features. At the coarsest resolution, a lightweight local attention module aggregates neighborhood information. The decoder mirrors this process, interleaving sparse residual convolutions with self-pruning upsample blocks to restore the original resolution and predict the Sparcubes parameters {&² 83. Each self- pruning block first predicts the occupancy of the subdivided voxel occupancy mask o, supervised by foce = BCF( o), then applies a learned upsampling to refine the voxel features. Because e is sign-sensitive (inside vs. outside), we split its prediction into sign branch and magnitude branch. The sign branch predicts sign(Ø) under figuign BCF(sign(()) (é), sign())), while the magnitude branch regresses @ with fermae = 11 øl12- The deformation vectors are optimized via Ls = |16,812. Finally, we regularize the latent distribution using the VAE's Kullback-Leibler divergence LKL (L(g(z)ō, d))lp(z)), yielding a single cohesive training objective that jointly minimizes the occupancy. sign, magnitude, deformation and KL divergence losses:
~= froce + >sign fignign + Amag f + A Lis + AKL LKL.
(7)
detailed description of the module design and the choice of are in the Supplementary Material.
Hole filling. Although predicted occupancy may be imperfect and introduce small holes, our inherently watertight Sparcubes representation allows for straightforward hole detection and filling. We first identify boundary half-edges. For each face = (vo. VI, V2). we emit the directed edges (Vo V1); (v1 + v2), and (v2 + vo). By sorting each pair of vertices into undirected edges and counting occurrences, edges whose undirected counterpart appears only once are marked as boundary edges. We build an outgoing-cud
map keyed by source vertex, and then recover closed boundary loops by walking each edge until returning to its start. To triangulate each boundary loop [v we follow a classic ear- ear-filling pipeline: compute a geometric score at every vertex, fill the "best" ear, and repeat until all open small boundaries vanish. Specifically, the score on each pending filled angle A, is defined by
4 = atan2 (1, x d.-s+ill2. -d,-1-+s d
(8)
In each iteration, we select the vertex with the smallest A; (i.e., the sharpest convex ear), form the triangle (vi-1; Vi, V++1) 1). and update the boundary. Merging all new triangles with the original face set closes every small hole.
4 Experiments
4.1 Experiment Settings
Implementation details. We implement all Sparcubes as custom CUDA kernels. Following TREL- LIS [32], train both the Sparconv- VAE and its latent flow model on 500 K high-quality assets from Objaverse [8] and Objaverse-XL [7]. The VAE runs on 32 A 100 GPUs (batch size 32) with AdamW (initial LR x 10-4) for two days. We then fine-tune the TRELLIS latent flow model on our VAF latents using 64 A 100 GPUs (batch size 64) for ten days. At inference, we sample with a classifier-free guidance scale of 3.5 over 25 steps, matching TRELLIS settings.
Dataset. Following Dora [2], we curated a VAE test set by selecting the most challenging examples from the ABO [6] and Objaverse [8]
those exhibiting occluded components, intricate geometric details, and open surfaces. To avoid any overlap with training data used in prior work, we additionally assembled "Wild" dataset with multiple components from online sources that disjoint from both ABO and Objaverse. For generation, we also benchmarked our method against TRELLIS [32] using the wild dataset.
Compared methods. We compare our Sparconv- -AR with the previous ofte-of-the art vaes, including TRELLIS [32], Craftsman [15]. Dora [2] and XCubces [24]. Because diffusion architecture and

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Table I: Quantitative comparison of watertight remeshing across the ABO [6], Objaverse [8], and In-the- Wild datasets. Chamfer Distance (CD, x x10"), Absolute Normal Consistency (ANC, x x10²) and Fl score (F1, x10²) are reported.
Method
ABO [6]
Objaverse [8]
Wild
CD
ANC 1
F1
CD 4
ANC
F1 f
CD 4
ANC
F1 f
Dora-wt-512 512 [2]
1.16
76,94
83.18
4.25
75.77
61.35
67.2
78.51
64.99
Dora-wt- (ane-tt-12244 024 [2]
1.07
76.94
84.56
4.35
75.04
63.84
63.7
78.77
65.90
Ours-wt-512
1.01
77.75
85.21
3.09
75.35
64.81
0.47
88.58
96.95
Ours-wt- 1024
1.00
77.66
85.39
3.01
74.98
65.65
0.46
88.55
97.06
Raw mesh
Ours-wt-512
Dora-wt-512
Ours-wi-100
Dora-wt-
Figure 4: Qualitative comparison of watertight remeshing pipelines. We evaluate our Sparcubes remeshing pipeline against previous widely used [2, 15, 39], i.e., Dora-wt [2], at voxel resolutions of 512 and 1024. Compared with the previous method, our Sparcubes preserves crucial components (e.g.. the car wheel) and recovers finer geometric details (e.g., the shelving frame). Our wt-512 result even outperforms the wt- 1024 remeshed by Dora-wt wt [2]. Best viewed with zoom-in.
model size match those of TRELLIS [32], we evaluate our generation results against to ensure a fair comparison.
4.2 Comparation Results
Watertight remeshing results. We evaluate both watertight remeshing (serving VAE ground truth) on the ABO [6], Objaverse [8], and Wild datasets, using Chamfer distance (CD), Absolute Normal Consistency (ANC), and Fl score (F1) metrics. As Table 1 shows,
Sparcubes consistently outperforms prior pipelines [2, 15, 39] (reported under "Dora-wt" [2] for brevity) across all datasets and metrics. Remarkably, 1t24 results produced by previous methods. Fig. 4 presents qualitative comparisons: our approach faithfully preserves critical components (e.g., the wheel) and recovers fine geometric details (e.g.. shelving frames).
wt-512 remeshed outputs exceed the quality of the
VAE reconstruction results. We further assess our Sparcony-VAL VAE reconstruction against TREL- LIS [32], Craftsman [15], Dora [2], and XCubes [24] in Table 2. Across the majority of datasets and metrics, our Sparconv- VAE outperforms these prior methods. Qualitative results in Fig. 5 illustrate that our VAE faithfully reconstructs complex shapes with fine details (all columns), converts open surfaces into double-layered watertight meshes (columns 4, and 6), and reveals unvisible hidden internal structures (column 6).
Generation results. We also valid the effectiveness of our Sparconv-VAN for generation by fine- tuning the TRELLIS [32] pretrained model. Under same diffusion architecture and model size (see Fig. 6), our approach synthesizes watertight 3D shapes with exceptional fidelity and rich detail-capturing. for example, the sharp ridges of pavilion eaves, the subtle facial features of human figures, and the intricate structural elements of robots.

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Table 2: Quantitative comparison of VAE reconstruction across the ABO [6], Objaverse [8], and In-the- Wild datasets. Chamfer Distance (CD, x10*), Absolute Normal Consistency (ANC, x10²) and Fl score (F1, x 10 reported.
Method
ABO [6] ANC
Objaverse [8]
Wild
CD
f
CD
ANC
F1 f
CD 4
ANC
F1 f
TRELLIS [32]
1.32
75.48
80.59
4.29
74.34
59.27
0.70
85.60
94.04
Craftsman [15]
1.51
77.46
77.47
2.53
77.37
55.28
87.81
92.28
Dora [2]
1,45
77.21
78.54
4.85
77.19
54.37
68.2
78.79
62.07
XCubes [24]
1.42
65.45
77.57
3.67
61.81
51.65
2.02
62.21
73.74
Ours-512
1.01
78.09
85.33
3.09
75.59
64.92
0.47
88.74
96.97
Ours-1024
1.00
77.69
85.41
3.00
75.10
65.75
0.46
88.70
97.12
Surface Error
Ours-512
Ours- 1024
TRELLI LIS
CraftsMan
Dora
XCube
Maarcon--VAE VAE demonstrates superior performance in reconstructing complex geometries, converting open surfaces into double- layered watertight meshes, and revealing unvisible internal structures. Best viewed with zoom-in.
Figure Qualitative comparison of VAE reconstructions. Our Sparconv-

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Condition image
Ours (front)
Ours (hack)
TRELLIS (front) TRELLIS (back)
Figure 6: Qualitative comparison of single-image-to-B1 generation. Under the same architecture and model size [32], the generator trained with our Sparconv-VAF yields more detailed reconstruc- tions than TRELLIS [32]. Best viewed with zoom-in.
Table 3:
Ablation study on Steps 3 and 4 in the preprocessing, Chamfer Distance (CD, x x10*).
Absolute Normal Consistency (ANC, x10*), and F1 score (F1, x x10²) are reported.
w/ Step 3 w/ Stepp
CD 1
ANC f
F1 1
0.97
77.34
88.13
0.95
77.81
88.73
0.92
78.08
89.52
4.3 Ablation Studies
Conversion cost. Compared existing remeshing methods [2, 15, 39], Sparcubes achieves substantial speedups: at 512-voxel resolution, conversion takes only around 39]-and at 1024-voxel resolution, completes in around 30 versus around 90 by [2, 15, 39]. Moreover, by climinating modality conversion in our VAE design, we avoid the additional SDF resampling step, which earlier pipelines adds roughly 20s 512 resolution and about 70s at 1024 resolution [2, 15, 39]. Detailed performance comparisons can be found in the Supplementary Material.
than [2, 15,
Training cost. Thanks to our modality-consistet design, Sparconv- VAE converges less than two days-about four times faster than previous methods, i.e., sparse voxel-based TRELLIS [32] and vecset-based approaches [2, 15] each require roughly seven days to train.
VAE with 2D rendering supervision. We also investigate the effect of incorporating 2D rendering losses into our VAE by using the mask, depth, and normal rendering objectives. We find that adding 2D rendering supervision yields negligible improvement for our Sparconv-VAE. This observation concurs with Dora [2], where extra 2D rendering losses were likewise deemed unnecessary for 3D-supervised VAEs. We attribute this the fact that sufficiently dense 2D renderings encode essentially the same information as the underlying 3D geometry each view being a projection the same 3D shape.
Ablation of each the preprocessing. We perform an ablation study on Steps 3 and isolate the contributions of deformation optimization and rendering- based refinement:

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In addition to consistent quantitative gains across all metrics, enabling both steps leads to substantial visual improvements often beyond what the metries alone can capture in terms of perceptual quality.
5 Conclusion
We introduce Spare3D, a unified framework that tackles two longstanding bottlenecks in 3D gen- eration pipelines: nopology-preserving remeshing and modality-conssitere latent encoding. At its heart, Sparcubes transforms raw, non-watertigh meshes into fully watertight surfaces at high resolution-retaining fine details and small components. Building on this, Spareonv-VAE, sparse- convolutional variational autoencoder with a self- self-pruning decoder, directly compresses and recon- structs our sparse representation without resorting to heavyweight attention, achieving tfate-of-the-are reconstruction fidelity and faster convergence. When coupled with latent diffusion (e.g.. TRELLIS), Spare3D elevates generation resolution for downstream 3D asset synthesis. Together, these contribu- tions establish robust, scalable foundation for high-fidelity 3D generation in both virtual (AR/VR, roboties simulation) and physical (3D printing) domains.
Limitations. While our Sparcubes remeshing algorithm excels at preserving fine geometry and exterior components, shares several drawbacks common prior methods. First, does not retain any original texture information. Second, when applied to fully closed meshes with internal structures, hidden elements will be discarded during the remeshing process.
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Reviews:
[{"review_id": "6NrkbtOHHE", "reviewer": "Reviewer_FtUJ", "summary": "This work introduces a unified framework called SparC, that combines sparse deformable marching cubes representation SparseCubes with a vae-baed encoder SparConv-VAE in order to address the challenges faced by the existing two stage pipelines that can result in a major loss of details through the vae processing. The method achieves sota reconstruction results and preserves fine details of the shapes, while maintaining efficient training and inference.", "questions": "1. Did you evaluate your method on other datasets, in addition to the ABO, Objaverse and Wild datasets?\n2. Were different seeds used in the quantitative comparison of Table 1? \n3. What is the impact of Step 4 of using multiple view images on the reconstruction / generation quality? Is there a visual comparison figure to show the impact?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well written and provides a good motivation to the proposed method.\n2. The literature review also neatly categorizes the existing methods and provides the pros and cons of the related work.\n3. The experiments have proper details of the metrics used and the baselines.\n4. Ablation studies are included to evaluate the speedup costs and training costs.\n\nWeaknesses:\n1. The method involves an optimization process in Step 3 and it would be helpful to understand the stability of this this optimization process. Simplest could be to show it over multiple seeds.\n2. A visual comparison of impact of multi-view vs single-view rendered images in step 4 would be helpful.", "was_revised": true, "final_justification": "I have reviewed the paper and I think it will be a good addition to Neurips. I have also reviewed my final scores for this.", "rebuttal": "We sincerely appreciate your valuable feedback and recognization of the contributions to our work. Due to the limitations of NeurIPS this year, we are unfortunately unable to upload images.\n\n**(W1) Stability of step 3**: We deeply appreciate the reviewer’s concern about the stability of our per-object optimization in Step 3. In this phase, we simultaneously refine each vertex’s signed distance function (SDF) value and its positional offset—an essential step for achieving precise watertight reconstruction. To guard against any mesh inversions or “face flips,” we enforce strict sign consistency on the SDF values, ensuring that vertices originally classified as inside or outside never change their label. Additionally, we cap each vertex’s movement to at most half of the original cube edge length, a mathematically proven safe bound that prevents any inverted topology. Thanks to our high-resolution initialization, where sparse cubes already densely cover the raw mesh surface, this carefully constrained optimization converges extremely quickly—typically < 10 steps.\n\nTo confirm the robustness of this design, we ran Step 3 one hundred times with different random seeds. We observed that the Chamfer Distance (×10⁴) varied by less than 0.01, demonstrating that our method is highly insensitive to stochastic initialization and reliably reproducible. We hope this extended analysis reassures the reviewer of the stability of our approach. Per your suggestion, we will include detailed convergence discussion of the optimization in the camera-ready manuscript.\n\n**(W2&Q3) Using multiple view images in Step 4** is necessary, as single-view rendered images cannot optimize the occluded regions. As a result, details in those unseen areas will be poor if only using single-view image.\n\n**(Q1) Additional datasets**: We apologize for the unclear description of our “in-the-wild” dataset. This collection combines the most challenging examples from multiple sources—including difficult cases from GSO and scanned or artist-created samples gathered online. In the camera-ready version, we will present a detailed breakdown of the dataset’s composition and selection criteria.\n\n**(Q2) Seed among different benchmarks:** We used the same random seed across all benchmarks to eliminate potential bias. Moreover, we repeated each experiment 100 times and observed that the Chamfer Distance ($\\times 10^4$) varies by less than 0.01 across runs, demonstrating that our results are robust to seed selection.\n\nThank you again for your insightful feedback; we hope our clarifications and planned enhancements address your concerns and further strengthen our submission."}, {"review_id": "caylWsVH0h", "reviewer": "Reviewer_Awed", "summary": "This paper introduces SparC, a unified framework for high-resolution 3D shape modeling that addresses key limitations in existing 3D generation pipelines. The work consists of two main components: SparseCubes, a sparse deformable marching cubes representation that converts raw meshes into watertight surfaces at $1024^3$ resolution, and SparConv-VAE, a VAE based on sparse convolution architecture achieved SOTA performance when trained on the high-quality processed watertight meshes.\n\nThe paper's main contributions are:\n- A watertight remeshing algorithm, i.e., SparCubes, that preserves fine details and small components while achieving nearly 3 times speedup over baseline methods.\n- A space-conv-based VAE for SparCubes encoding.\n- State-of-the-art 3D shape reconstruction quality.", "questions": "Please refer to major weaknesses for more details.\n\n- Remesh algorithm comparisons and ablation study.\n- The novelty of sparconv-vae is not clear to me.\n- Model architecture and training details are missing.\n\nOverall, I think this paper is not ready for publication in NeurIPS. I will consider raise my score if the authors can address my questions.", "limitations": "While the authors mention some limitations in their conclusion section (texture loss and internal structure handling), the limitations discussion is insufficient and should be expanded to address several critical aspects:\n\n- Failure case analysis. The paper lacks discussion of failure modes. For example, what types of mesh structures cause the deformation optimization to converge poorly?\n- The technical limitations of the proposed remeshing algorithm is not clear and should be analyzed.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The paper is well-written and easy to follow.\n- The paper proposes an engineering solution to address the high-resolution watertight remeshing problem, which is a key bottleneck in current 3D generation workflows.\n- The paper achieves state-of-the-art reconstruction results both quantitatively and qualitatively across several benchmarks\n\n### Major Weaknesses\n\n- While this work focuses on remeshing algorithms, it only compares against Dora's remesh algorithm, which is a baseline implementation in their open-source code. The work should compare against more advanced remeshing algorithms and provide detailed analysis of advantages and disadvantages.\n- SparCubes involves 4 steps, but the ablation study lacks analysis of individual components. For example, the actual contribution of deformation optimization and rendering refinement steps is unclear to reviewer.\n- TRELLIS already adopts sparse convolution, but the differences and advantages of SparConv-VAE compared to TRELLIS's VAE are not clearly illustrated. It is hard to determine whether the improvements over TRELLIS mainly come from higher-quality remesh data or VAE architecture design.\n- The paper lacks clear descriptions of VAE and latent flow model details, including model architecture and latent resolution. Moreover, TRELLIS adopts a two-stage training approach - it's unclear whether this work follows a similar scheme.\n\n**Minor weaknesses**\n- Section 3.3 mentions hole filling but doesn't clearly explain where this algorithm is applied or demonstrate its effectiveness.\n- No analysis of SparCubes' resolution bottlenecks and failure cases.\n- The contribution of each loss term during vae model training is unclear.", "was_revised": true, "final_justification": "After careful consideration of the authors' responses, I am updating my evaluation and raising my score to borderline accept. While some areas still warrant further improvement and more detailed explanations, the authors have adequately addressed the majority of the concerns raised during the review process.\n\nHowever, I strongly recommend that the authors follow through on their promises to:\n\n- Incorporate the remeshing algorithm ablation studies into the main paper, as these studies are crucial for understanding the method's performance\n- Include detailed model architecture descriptions in the main paper to enhance reproducibility and technical clarity\n- Add more comprehensive remeshing visualization comparisons to better demonstrate the qualitative improvements achieved by the proposed method\n\nThese additions would significantly strengthen the paper's contribution and make it more accessible to the research community.", "rebuttal": "We appreciate the time and effort dedicated to evaluating our work, and we have addressed each point raised with detailed clarifications\n\n**(W1) Additional Baselines:**\nWe have compared our method against the state-of-the-art remeshing techniques available at the time of submission (e.g., Dora). In this rebuttal, we also include results for Mesh2SDF [1] on the most challenging cases. We would be happy to add further comparisons if the reviewers can suggest any other readily available, advanced remeshing algorithms.\n\n|Name|\tCD ↓ (×10⁴)|\tANC ↑ (×10²)|\tF1 ↑ (×10²)|\n|:---|:--:|:--:|:--:|\n|Dora|\t1.32|\t75.23|\t79.10|\n|Mesh2SDF [1]|\t2.10|\t72.19|\t76.19|\n|Ours |\t0.92|\t78.08|\t89.52|\n\n**(W2) Ablation of each in the preprocessing:**\n\nWe perform an ablation study on Steps 3 and 4 to isolate the contributions of deformation optimization and rendering-based refinement:\n\n|w/ Step 3|\tw/ Step 4|\tCD (×10⁴) ↓|\tANC (×10²) ↑|\tF1 (×10²) ↑|\n|:--:|:--:|:--:|:--:|:--:|\n|✓|\t\t|0.97|\t77.34|\t88.13|\n|\t|✓\t|0.95|\t77.81|\t88.73|\n|✓\t|✓\t|0.92|\t78.08|\t89.52|\n\nIn addition to consistent quantitative gains across all metrics, enabling both steps leads to substantial visual improvements—often beyond what the metrics alone can capture in terms of perceptual quality. Due to limitations of rebuttal this year, we cannot include these visuals here; however, we will present comprehensive side-by-side comparisons in the camera-ready version.\n\n\n**(W3) Comparsion with the Trellis' VAE:**\nWe would like to clarify that TRELLIS's VAE is based on sparse transformers rather than sparse convolutions. Its architecture is fully composed of Swin attention blocks, which are not scalable or trainable at high resolutions (e.g., 1024³), due to the prohibitive memory and computational cost. This makes direct comparison with our approach technically infeasible.\n\nOur VAE, in contrast, offers significant advantages: It achieves extremely high compression ratios while maintaining superior reconstruction quality and effective learning for downstream generation tasks. Specifically, it supports decoding from a latent resolution of $64^3$ to $1024^3$, significantly surpassing the $64^3→256^3$ decoding capability in TRELLIS.\n\nWe did not include an explict concept comparison with the TRELLIS VAE because the two are fundamentally different in terms of representation space, training objectives, and architecture. For example:\n\n- Our VAE learns native geometric representations, whereas TRELLIS relies on lossy DINO features.\n- TRELLIS lacks sparse downsampling and upsampling modules, making it infeasible to handle high-resolution sparse inputs. Dense occupancy grids at this scale become too large to train/inference and result in out-of-memory (OOM) issues.\n- We use supervision in the 3D space while Trellis use 2D supervsion from the image space.\n\nLastly, we would like to emphasize that our remeshing pipeline is itself a key contribution of this work, and forms an integral part of the overall design.\n\n**(W4) Detailed architecture:** The detailed design of our VAE is provided in the supplementary material. As noted in the paper, the architecture of the generative component is identical to that of TRELLIS, and we adopt the same two-stage design.\n\n**(MW1) Where to apply hole-filling:** We apply hole-filling to the outputs of the second stage—that is, the final generated results. While this does not affect the VAE evaluation metrics, it can influence the visual quality of the generated meshes.\n\n**(MW2) Resolution bottleneck and failure cases:** The resolution of SparCubes is currently limited by the nvdiffrast rendering backend. In particular, nvdiffrast imposes a fixed upper limit on the number of mesh fragments due to its internal mesh ID encoding. Therefore, we could not directly applied it to the resolution of $2048^3$. \nHowever, this is a constraint of the underlying renderer rather than our framework design.\n\nAs for failure cases, SparCubes may struggle with meshes that contain extremely occluded internal parts. In such cases, Step 4 may not be effective due to the lack of visibility during optimization. While Steps 1–3 can still provide a coarse reconstruction of these hidden areas, the fidelity may not match that of the visible surfaces.\n\nHowever, compared to Trellis, SparCubes demonstrates significantly better internal structure reconstruction. This is because our Steps 1–3 are unaffected by occlusion and do not rely on 2D rendering loss for supervision. For example, the internal details of the robot in the teaser figure are well preserved in our results, whereas Trellis—being solely guided by 2D supervision—tends to lose such internal structures.\n\n\n**(MW3) Ablation of each training loss**\nWe validated the effectiveness of each loss term under a small-scale dataset prior to the scaled-up training. As with many large-model frameworks, conducting a full ablation study may be impractical due to computational constraints.\n\n\n**Technical limitations**\n- We observed that, apart from the commonly known challenge of handling highly occluded internal structures—which remains difficult for most methods—our approach does not exhibit any significant additional limitations in terms of the remeshing quality compared to existing methods.\n- Similar to existing methods, our use of Marching Cubes can result in meshes with a high number of faces. In certain application scenarios, this may require additional post-processing, which is also a common open problem in the community. Nonetheless, we believe our compact latent representation could benefit downstream retopology tasks, making this a promising direction for future exploration.\n\n**Reference:**\n\n[1] Peng-Shuai Wang, Yang Liu, and Xin Tong. 2022. Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations. ACM Trans. Graph. (SIGGRAPH) 41, 4, Article 103 (July 2022), 14 pages."}, {"review_id": "rDWsAfwT1b", "reviewer": "Reviewer_6geV", "summary": "The paper proposes a new 3D representation for reconstruction and generation, named SparCubes. The representation builds upon sparse and deformable vertices and cubes. Different from the original marching cubes, the sparse vertices are optimized to move to the boundary of meshes. The SDF values and position differences are further optimized by 2D depth and mask renderings. Based on this representation, the paper presents a VAE and a subsequent diffusion model on the vae latents.", "questions": "- In the step 3 of SparCubes reconstruction, are the SD values on vertices also optimized along with the delta positions? Are all vertices optimized to move towards the boundary? If it is the case, does the spatial grouping of many vertices lead to unstable optimization, such as face flipping?\n- In eq 5. does the delta function mean the per vertex shift? What does approximately equal mean here, does it mean that there is a constraint on the movement of the vertex positions?\n- Reference [28] (TripoSR) is not a diffusion model but a LRM. In this context, it seems to make more sense to replace it with \"TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models\".\n- \"x\" in Eq 3. should be math-bolded.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The proposed method is effective in converting meshes with open surfaces to watertight meshes, which is crucial in 3D generative modeling. The remeshing process brings little loss to model accuracy.\n- By converting the meshes to the SDF-based reprensentation, it facilitates the training of the 3D VAEs, involving no modality conversion but only compressing. \n- The reconstruction and generation results look superior than previous methods.\n\nWeaknesses:\n- SparCubes is implemented with custom CUDA kernels. I have concerns about the reproducibility if the code is not open sourced. \n- In the SparCubes reconstruction pipeline, the step 3-4 involve per-object optimization, which seems time-consuming. I am not sure whether the claimed time (line 63) includes these steps. \n- SparseFlex is not compared in the main paper, especially under quantitative evaluations.\n- Some technical details are missing. Please see questions below.", "was_revised": true, "final_justification": "The authors address most of my concerns in rebuttal. The paper presents a novel approach in encoding and generating 3D meshes and achieves better reconstruction quality compared to previous methods. Despite the required per-object optimization, I still find the 3D representation valuable for the community. So I will maintain my rating as Accept.", "rebuttal": "We thank Reviewer 6geV for the thoughtful and constructive feedback, and for recognizing the value of our work.\n\nBelow, we provide our detailed response:\n\n**(W1) Reproducibility of custom CUDA kernels:** As researchers, we wholeheartedly agree that open-source is essential for the community. We are currently seeking internal approval and, once granted, will release the code publicly. Moreover, in the camera-ready version, we will include a detailed description of our CUDA kernels to ensure full reproducibility.\n\n**(W2) Optimization cost in remesh steps 3–4:** Our reported timing already includes the optimization overhead for steps 3–4. Thanks to the cubes’ high initial resolution—where they closely align with the target surface—each cube requires only a few updates:\n- Step 3: converges in fewer than 10 gradient updates.\n- Step 4: converges in approximately 100 gradient updates.\n\nThis efficiency enables the full per-object optimization to complete in roughly 30 seconds.\n\n**(W3) Comparison to SparseFlex:** We appreciate the reviewer’s suggestion. SparseFlex is an concurrent arXiv preprint (uploaded on March 27, 2025), just prior to our NeurIPS submission deadline, so we originally placed the quantitative comparison in the supplementary material. In the camera-ready version, we will move this comparison into the main manuscript for greater visibility.\n\nBelow, we report Chamfer Distance (CD) and runtime under challenging evaluation settings:\n\n|Method | CD ($\\times10^4$) ↓ | Token Num ↓ | Enc Dec Time (s) ↓ |\n|:---|:---:|:---:|:---:|\n|Ours | 0.88 | 46047| 2.2 |\n|SparseFlex | 0.87 | 265601| 13.0 |\n\nAs shown, our method achieves a comparable Chamfer Distance while using ~6× fewer tokens and delivering ~6× faster encoding–decoding, consistent with the qualitative results presented in the supplementary material.\n\n\n**(Q1) Details about remeshing optimzation:** We jointly optimize per-vertex SDF offsets and positional deltas, enforce sign consistency on the SDF (so that positive/negative labels never flip), and constrain each vertex’s movement to within half of the original cube edge—provably preventing any inverted faces. We will make these details explicit in the camera-ready version.\n\n**(Q2) Typo in Eq. 5:** We apologize for the typo in Eq. 5: the expression $ \\delta(x) \\sim \\delta(x) $ was included by mistake and will be removed. We did not rely on this assumption.\n\n\n**(Q3) Typo in Reference [28] (TripoSR):** We thank the reviewer for catching this error. In the camera-ready version, we will correct the citation to:\n\n''TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models.''\n\n\n**(Q4) Formatting in Eq. 3:** We appreciate the reviewer’s careful attention to detail. We will boldface 'x' in Eq. 3 in the camera-ready manuscript."}, {"review_id": "aBA8HwhOpj", "reviewer": "Reviewer_L8c7", "summary": "This paper presents SparCubes, a fast and near-lossless remeshing algorithm that produces watertight 3D surfaces at high resolution in just 30 seconds, offering a 3× speedup over prior work. The authors also propose SparConv-VAE, a modality-consistent variational autoencoder with a sparse convolutional encoder and a self-pruning decoder, achieving efficient and accurate reconstruction without relying on global attention.", "questions": "- How does utilizing a sparse set of active voxels maintain 3D reconstruction accuracy compared to processing a dense voxel grid across the entire bounding volume?\n\n- How can flood-fill algorithms reliably distinguish between interior and exterior regions when the target object lacks watertight closure during initial training stages?\n\n- Could directly optimizing the geometric structure of sparse cubes instead of explicitly refining a globally accurate SDF lead to loss of fine surface details?\n\n- What are the advantages of decoupling SDF prediction into sign classification (using BCE loss) and magnitude regression (L2 loss)? Has end-to-end SDF regression with sign-aware loss functions been explored as an alternative?\n\n- In Table 2, many of the metrics fall short compared to previous work. The authors should provide an analysis to explain these performance gaps.\n\n- For the visualization comparisons, the authors should include more published methods as baselines, such as XCubes.", "limitations": "Yes", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- The authors propose a method to address high-resolution 3D shape modeling, which is an interesting and relevant topic. Moreover, they conduct extensive experiments to validate the effectiveness of the proposed approach. In addition, the proposed method achieves this with less computational time compared to existing methods.\n\n- The use of sparse voxels over dense grids lacks sufficient justification, and directly optimizing sparse structures may lead to loss of fine details.\n\n- The reliability of the flood-fill algorithm on non-watertight shapes is questionable, and the decoupled SDF prediction lacks comparison with end-to-end alternatives.\n\n- Several metrics in Table 2 fall behind prior work without explanation, and the visualization comparisons lack key baselines such as XCubes.\n\n- The current method struggles to preserve fine details of the target and is prone to losing small or hidden components, making it challenging to apply in practical projects.", "was_revised": true, "final_justification": "Thanks very much for the rebuttal. After reading the rebuttal and other reviews, I keep my original rating. Specifically, I am not quite convinced with the answer to my last question.", "rebuttal": "We would like to thank for your comments. Below, we address each of your concerns in detail.\n\n**(W1&Q1) The motivation of using sparse voxels:** The motivation for using sparse voxels is both well-founded and straightforward. Dense grids face significant limitations in terms of memory and computational efficiency. Direct optimization in dense grids becomes infeasible due to out-of-memory issues when the resolution exceeds $256^3$, making it practically impossible to operate at resolutions like $1024^3$.\n\nOur sparse voxel representation does not compromise fine details. In the ideal case, a dense grid activates voxels along the surface of the raw mesh—precisely what our encoder achieves with sparse cubes. Thanks to the high efficiency of our encoder, we can afford much higher resolutions than dense grids, resulting in superior reconstruction and generation performance.\n\n**(W2) The reliability of the flood-fill algorithm:** Apart from our flood fill approach, the previously most common method for determining inside/outside regions in non-watertight remeshing is computing the winding number. However, winding number requires the input mesh to have correctly oriented normals—an assumption that is often violated, especially in large-scale mesh datasets from the internet where normal directions are frequently incorrect or inconsistent. In contrast, our flood fill method only requires the initial seed point to be outside the surface (e.g., placed in a corner), making it a more stable and controllable solution.\n\n**(W3&Q4) The metric that fall behind:** ANC on Objeverse is one of the few metrics where our method does not consistently outperform others. However, ANC evaluates absolute normal consistency against the ground-truth normals, which are often noisy or incorrect. Our method, on the other hand, corrects the normal orientations via flood fill. Therefore, this metric may not accurately reflect the actual surface quality. Moreover, as shown in the visual results from Objaverse in Fig. 5, our method preserves significantly more geometric details than CraftMan—beyond what is indicated by the quantitative metrics.\n\n\n**(W4) Fine details and small or hidden components:**\nAs highlighted in our paper, e.g., the teaser figure demonstrates rich details and hidden components, we argue that our method more effectively preserves fine details, including those in occluded regions. Furthermore, as shown in Fig. 5, our approach retains small and hidden structures without loss compared with the existing SOTA method. These components are already well captured during our multi-step process (Steps 1–3). Compared to prior methods using 2D supervision like Trellis, our approach demonstrates significantly better preservation of hidden parts, as the 2D rendering loss used provides very weak supervision for unobserved areas. Unlike many existing open-source and even commercial text-to-3D models, our generated meshes are strictly watertight, making them suitable for practical applications such as 3D printing.\n\n**(W4&Q5) Visulization of XCubes:**\nWe already provided the visualization results of XCubes in the last row of Fig. 5 of the main paper. \n\n**(Q2) Flood-fill for the object w/o watertight closure:**\nWe address this challenge using a coarse-to-fine multi-scale flood-fill strategy. Even when the target object is not fully watertight during early training stages, the coarsest scale provides a sufficiently smoothed and regularized representation that enables robust separation between interior and exterior regions. This coarse-level initialization ensures reliable flood-fill propagation, which is then progressively refined at finer scales.\n\n**(Q3) Refining a globally accurate SDF:**\nOur globally accurate SDF is in fact obtained through local optimization over high-resolution sparse cubes, rather than by fitting a global function directly. Therefore, it does not suffer from the loss of fine surface details.\n\nThanks to the high resolution of our sparse representation, local SDF refinement is both efficient and precise. Moreover, the sparse cubes are tightly aligned with the raw mesh surface, effectively covering the local geometry. Optimizing these cubes is essentially equivalent to optimizing accurate local SDFs, which preserves detailed structures.\n\n**(Q4) Decoupled SDF prediction:** As with other large-scale generative models, conducting a comprehensive ablation study on each individual loss term is infeasible. Nevertheless, we argue that decoupling the sign and magnitude of the SDF is well justified for two reasons:\n\n- **Empirical success in 3D vision.** Prior work on implicit surface learning (e.g., Points2Surf [1]) has demonstrated that separating the prediction of sign (inside vs. outside) from the prediction of distance yields significantly better results than treating the SDF as a single end-to-end regression target.\n- **Faster convergence for classification vs. regression.** Statistical learning theory shows that, under low-noise (margin) conditions, classification can achieve strictly faster minimax convergence rates than regression. In particular, Audibert and Tsybakov [2] construct plug-in classifiers that attain “fast” and even “super-fast” rates—up to $O(n^{-1}$)—for the excess Bayes risk when the conditional probability function satisfies the Tsybakov margin assumption.\n\nWe hope that our responses adequately address your concerns and demonstrate the soundness of our approach.\n\n**References:**\n\n[1] Erler, P., Guerrero, P., Ohrhallinger, S., Mitra, N. J., & Wimmer, M. (2020). Points2Surf: Learning implicit surfaces from point clouds. In ECCV (pp. 108–124). Springer.\n\n[2] Audibert, J.-Y., & Tsybakov, A. B. (2007). Fast learning rates for plug-in classifiers."}]

Author rebuttal:

Meta-review: The AC and the reviewers thank the authors for their response.
This paper, through the combination of SparseCubes and SparConv-VAE, achieves for the first time a consistent input-output modality with nearly lossless 3D representation and reconstruction, enabling high-resolution 3D reconstruction and generation.

After the rebuttal, most of the reviewers’ concerns were addressed. In the end, two reviewers leaned toward direct acceptance, one leaned toward borderline acceptance, while another reviewer (L8c7) still had concerns regarding the last issue and gave a borderline rejection.

After carefully reading the paper, the reviewers’ comments, and the authors’ response, the AC believes that the contributions of this paper are significant. Although the final generation results still have some artifacts and the surfaces are not sharp enough, the effectiveness of SparseCubes as a novel 3D data processing method, along with the efficiency of SparConv-VAE, are both impressive.

The AC recommends that the authors revise the issus, add more details in the paper, and further address the reviewers’ suggestions. With these improvements, the paper can be accepted.

The AC thanks both the reviewers and the authors for their efforts, and recommends acceptance this paper.
Decision: Accept (poster)