DualPrim: Compact 3D Reconstruction with Positive and Negative Primitives

Xiaoxu Meng; Zhongmin Chen; Bo Yang; Weikai Chen; Weixiao Liu; Lin Gao

cs.CVMar 17, 2026

DualPrim: Compact 3D Reconstruction with Positive and Negative Primitives

Authors: Xiaoxu Meng, Zhongmin Chen, Bo Yang, Weikai Chen, Weixiao Liu, Lin Gao

Organizations: Independent Researcher · Institute of Computing Technology, Chinese Academy of Sciences · School of Advanced Interdisciplinary Science, University of Chinese Academy of Sciences · Waymo LLC ·

Abstract

Neural reconstructions often trade structure for fidelity, yielding dense and unstructured meshes with irregular topology and weak part boundaries that hinder editing, animation, and downstream asset reuse. We present DualPrim, a compact and structured 3D reconstruction framework. Unlike additive-only implicit or primitive methods, DualPrim represents shapes with positive and negative superquadrics: the former builds the bases while the latter carves local volumes through a differentiable operator, enabling topology-aware modeling of holes and concavities. This additive-subtractive design increases the representational power without sacrificing compactness or differentiability. We embed DualPrim in a volumetric differentiable renderer, enabling end-to-end learning from multi-view images and seamless mesh export via closed-form boolean difference. Empirically, DualPrim delivers state-of-the-art accuracy and produces compact, structured, and interpretable outputs that better satisfy downstream needs than additive-only alternatives.

Figures & tables

Panel of Figure 2 — Figure 2: The first row shows the artist-created meshes (AMs), and the second row shows the exportation from Marching Cubes [ 30 ] .

Figure 3: The overview of DualPrim pipeline. We represent the 3D scene as a set of primitive parameters and optimize them within a differentiable rendering framework under multi-view image supervision. The final mesh is obtained by computing the Boolean difference between the positive and negative superquadric components of each primitive.

Parameter Name	Shape	Explanation	Range
scale parameters $(a_{x},a_{y},a_{z})$	$(2,3)$	The radius of the x-, y-, z-axis of PSQ and NSQ.	$[0.02,1.0]$
shape parameters $({\epsilon}_{1},{\epsilon}_{2})$	$(2,2)$	The parameters controlling the shape of PSQ and NSQ.	$[0.05,2]$
primitive transparency $\alpha$	$(1)$	The transparency of the super-primitive.	$[0,1]$
primitive render sharpness $\theta$	$(1)$	The sharpness of the super-primitive.	$[0,1]$
translation $T$	$(2,3)$	The translation of the PSQ and the NSQ.	$[-1,1]$
rotation $R$	$(2,3)$	The rotation angles of the PSQ and the NSQ.	$[-180,180)$

Table 1: The full parameter list of a super primitive.

Figure 4: Illustration of the SDF calculation with PSQ and NSQ.

Figure 5: We show the rendering of three example dual-primitives.

Panel of Figure 6 — Figure 6: Qualitative comparison on in-the-wild objects. Each object occupies two rows. The first row shows input, Ours, PA, MA, MP, EMS, and NVD. The second row shows FC, RNS, CAPRI, D 2 CSG, DiffCSG, 2DGS+MC, and 2DGS+CSG.

Panel of Figure 7 — Figure 7: The ablation study on regularization terms.

Method	#V(k) $\downarrow$	#F(k) $\downarrow$	NC Loss $\downarrow$	CD $\downarrow$	EditScore $\uparrow$
RNS [ 7 ]	124.41	57.44	37.45	10.71	4.77
NVD [ 36 ]	15.40	13.98	56.19	10.63	5.47
FC [ 47 ]	16.67	8.33	16.11	13.32	4.98
PA [ 62 ]	21.51	12.61	4.48	11.57	4.59
MP [ 28 ]	3.38	1.75	12.42	12.58	4.88
MA [ 10 ]	0.55	0.29	4.00	10.12	5.12

Table 2: Quantitative comparison and user evaluation of DualPrim (ours) with 13 representative methods.

Superquadric Representation

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Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods struggle to produce high-quality meshes. Most state-of-the-art approaches initially learn an intermediate representation (NeRF/3DGS) and treat mesh extraction as a post-processing step, which often leads to oversmoothed surfaces or poor quality meshes with excessive triangle counts.Existing mesh-native optimization methods alleviate some of these issues but suffer from fixed-resolution discretizations and unstable optimization behavior. In this paper, we introduce an adaptive mesh-based optimization framework and a practical mesh rendering technique to address these challenges. Our representation combines an optimizable Delaunay-triangulated tetrahedral grid with a multi-resolution hash grid. The former is refined through point pruning and insertion, while the latter provides latent features for SDF/appearance value predictions. We use volumetric rendering to bootstrap a coarse geometry while leveraging mesh-based rendering for recovering fine-grained details. Additionally, we propose a differentiable, rasterization-based depth-offset rendering formulation, reducing geometric artifacts and improving reconstruction quality. Our method significantly outperforms existing mesh optimization approaches across a variety of object-centric benchmarks while being competitive with state-of-the-art NeRF/3DGS methods.

Abstract

Superquadric Representation

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