GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
- URL: http://arxiv.org/abs/2410.01535v2
- Date: Sun, 6 Oct 2024 08:58:10 GMT
- Title: GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
- Abstract summary: We propose a novel part-aware compositional reconstruction method, called GaussianBlock, that enables semantically coherent and disentangled representations.
Our reconstructed scenes are evidenced to be disentangled, compositional, and compact across diverse benchmarks, enabling seamless, direct and precise editing.
- Score: 14.683337059919692
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, with the development of Neural Radiance Fields and Gaussian Splatting, 3D reconstruction techniques have achieved remarkably high fidelity. However, the latent representations learnt by these methods are highly entangled and lack interpretability. In this paper, we propose a novel part-aware compositional reconstruction method, called GaussianBlock, that enables semantically coherent and disentangled representations, allowing for precise and physical editing akin to building blocks, while simultaneously maintaining high fidelity. Our GaussianBlock introduces a hybrid representation that leverages the advantages of both primitives, known for their flexible actionability and editability, and 3D Gaussians, which excel in reconstruction quality. Specifically, we achieve semantically coherent primitives through a novel attention-guided centering loss derived from 2D semantic priors, complemented by a dynamic splitting and fusion strategy. Furthermore, we utilize 3D Gaussians that hybridize with primitives to refine structural details and enhance fidelity. Additionally, a binding inheritance strategy is employed to strengthen and maintain the connection between the two. Our reconstructed scenes are evidenced to be disentangled, compositional, and compact across diverse benchmarks, enabling seamless, direct and precise editing while maintaining high quality.
Related papers
- Incremental Online Scene Reconstruction by 3D Gaussian Triangulation [22.398131643187384]
We propose a novel online framework that incrementally reconstructs and updates high-fidelity explicit meshes.<n>We also present a direct meshing algorithm that efficiently extracts and updates the mesh from the Gaussian set.<n>Our framework significantly reduces memory and computational overhead during long-sequence processing.
arXiv Detail & Related papers (2026-07-12T10:22:18Z) - StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation [57.06461272772509]
StdGEN++ is a novel and comprehensive system for generating high-fidelity, semantically decomposed 3D characters from diverse inputs.<n>It achieves state-of-the-art performance, significantly outperforming existing methods in geometric accuracy and semantic disentanglement.<n>The resulting structural independence unlocks advanced downstream capabilities, including non-destructive editing, physics-compliant animation, and gaze tracking.
arXiv Detail & Related papers (2026-01-12T15:41:27Z) - Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
arXiv Detail & Related papers (2026-01-05T18:33:50Z) - C3G: Learning Compact 3D Representations with 2K Gaussians [55.04010158339562]
Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding.<n>We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations.
arXiv Detail & Related papers (2025-12-03T17:59:05Z) - GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering [50.675710727721786]
We propose GauSSmart, a hybrid method that bridges 2D foundational models and 3D Gaussian Splatting reconstruction.<n>Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision.<n>We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting.
arXiv Detail & Related papers (2025-10-16T03:38:26Z) - FMGS-Avatar: Mesh-Guided 2D Gaussian Splatting with Foundation Model Priors for 3D Monocular Avatar Reconstruction [18.570290675633732]
We introduce Mesh-Guided 2D Gaussian Splatting, where 2D primitives are attached directly to template mesh faces with constrained position, rotation, and movement.<n>We leverage foundation models trained on large-scale datasets, such as Sapiens, to complement the limited visual cues from monocular videos.<n> Experimental evaluation demonstrates superior reconstruction quality compared to existing methods, with notable gains in geometric accuracy and appearance fidelity.
arXiv Detail & Related papers (2025-09-18T08:41:41Z) - RGE-GS: Reward-Guided Expansive Driving Scene Reconstruction via Diffusion Priors [54.81109375939306]
RGE-GS is a novel expansive reconstruction framework that synergizes diffusion-based generation with reward-guided Gaussian integration.<n>We propose a reward network that learns to identify and prioritize consistently generated patterns prior to reconstruction phases.<n>During the reconstruction process, we devise a differentiated training strategy that automatically adjust Gaussian optimization progress according to scene converge metrics.
arXiv Detail & Related papers (2025-06-28T08:02:54Z) - Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization [27.509109317973817]
3D Gaussian Splatting (3DGS) has garnered significant attention for its high-quality rendering and fast inference speed.
Previous methods primarily focus on geometry regularization, with common approaches including primitive-based and dual-model frameworks.
We propose CarGS, a unified model leveraging-adaptive regularization to achieve simultaneous, high-quality surface reconstruction.
arXiv Detail & Related papers (2025-03-02T12:51:38Z) - G2SDF: Surface Reconstruction from Explicit Gaussians with Implicit SDFs [84.07233691641193]
We introduce G2SDF, a novel approach that integrates a neural implicit Signed Distance Field into the Gaussian Splatting framework.
G2SDF achieves superior quality than prior works while maintaining the efficiency of 3DGS.
arXiv Detail & Related papers (2024-11-25T20:07:07Z) - Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis [28.3325478008559]
We propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure.
We optimize the scene structure in two folds: rendering geometry and, more importantly, the position of Gaussian primitives.
Experiments on forward-facing, surrounding, and complex large scenes show the effectiveness of our approach with state-of-the-art performance and high efficiency.
arXiv Detail & Related papers (2024-11-06T03:28:06Z) - MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
arXiv Detail & Related papers (2024-10-15T08:39:05Z) - 2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Reconstruction [7.787937866297091]
2D Gaussian Splatting has recently emerged as a significant method in 3D reconstruction.
We propose to use the Gaussian-Hermite kernel as the new primitive in Gaussian splatting.
Our experiments demonstrate the extraordinary performance of Gaussian-Hermite kernel in both geometry reconstruction and novel-view synthesis tasks.
arXiv Detail & Related papers (2024-08-30T03:04:11Z) - Implicit Gaussian Splatting with Efficient Multi-Level Tri-Plane Representation [45.582869951581785]
Implicit Gaussian Splatting (IGS) is an innovative hybrid model that integrates explicit point clouds with implicit feature embeddings.
We introduce a level-based progressive training scheme, which incorporates explicit spatial regularization.
Our algorithm can deliver high-quality rendering using only a few MBs, effectively balancing storage efficiency and rendering fidelity.
arXiv Detail & Related papers (2024-08-19T14:34:17Z) - Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting [33.01987451251659]
3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction.
Despite its potential, 3DGS encounters challenges, including needle-like artifacts, suboptimal geometries, and inaccurate normals.
We introduce effective rank as a regularization, which constrains the structure of the Gaussians.
arXiv Detail & Related papers (2024-06-17T15:51:59Z) - R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction.
This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
arXiv Detail & Related papers (2024-05-31T08:39:02Z) - Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes.
GOF is derived from ray-tracing-based volume rendering of 3D Gaussians.
GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2024-04-16T17:57:19Z) - latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction [48.86083272054711]
latentSplat is a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture.
We show that latentSplat outperforms previous works in reconstruction quality and generalization, while being fast and scalable to high-resolution data.
arXiv Detail & Related papers (2024-03-24T20:48:36Z) - HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression [55.6351304553003]
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis.
We propose a Hash-grid Assisted Context (HAC) framework for highly compact 3DGS representation.
Our work is the pioneer to explore context-based compression for 3DGS representation, resulting in a remarkable size reduction of over $75times$ compared to vanilla 3DGS.
arXiv Detail & Related papers (2024-03-21T16:28:58Z) - GVGEN: Text-to-3D Generation with Volumetric Representation [89.55687129165256]
3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities.
This paper introduces a novel diffusion-based framework, GVGEN, designed to efficiently generate 3D Gaussian representations from text input.
arXiv Detail & Related papers (2024-03-19T17:57:52Z) - UGMAE: A Unified Framework for Graph Masked Autoencoders [67.75493040186859]
We propose UGMAE, a unified framework for graph masked autoencoders.
We first develop an adaptive feature mask generator to account for the unique significance of nodes.
We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information.
arXiv Detail & Related papers (2024-02-12T19:39:26Z) - GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation [35.39887092268696]
This paper presents a framework to model the actional human head with anisotropic 3D Gaussians.
In experiments, our method can produce high-fidelity renderings, outperforming state-of-the-art approaches in reconstruction, cross-identity reenactment, and novel view synthesis tasks.
arXiv Detail & Related papers (2023-12-04T05:24:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.