Binary-Gaussian: Compact and Progressive Representation for 3D Gaussian Segmentation

• URL: http://arxiv.org/abs/2512.00944v1
• Date: Sun, 30 Nov 2025 15:51:30 GMT
• Title: Binary-Gaussian: Compact and Progressive Representation for 3D Gaussian Segmentation
• Authors: An Yang, Chenyu Liu, Jun Du, Jianqing Gao, Jia Pan, Jinshui Hu, Baocai Yin, Bing Yin, Cong Liu,
• Abstract summary: 3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation.<n>We propose a coarse-to-fine binary encoding scheme for per-Gaussian category representation, which compresses each feature into a single integer via the binary-to-decimal mapping.<n>We further design a progressive training strategy that decomposes panoptic segmentation into a series of independent sub-tasks, reducing inter-class conflicts and thereby enhancing fine-grained segmentation capability.
• Score: 83.90109373769614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation. However, existing 3D-GS-based segmentation methods typically rely on high-dimensional category features, which introduce substantial memory overhead. Moreover, fine-grained segmentation remains challenging due to label space congestion and the lack of stable multi-granularity control mechanisms. To address these limitations, we propose a coarse-to-fine binary encoding scheme for per-Gaussian category representation, which compresses each feature into a single integer via the binary-to-decimal mapping, drastically reducing memory usage. We further design a progressive training strategy that decomposes panoptic segmentation into a series of independent sub-tasks, reducing inter-class conflicts and thereby enhancing fine-grained segmentation capability. Additionally, we fine-tune opacity during segmentation training to address the incompatibility between photometric rendering and semantic segmentation, which often leads to foreground-background confusion. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art segmentation performance while significantly reducing memory consumption and accelerating inference.

Related papers

• UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning [6.502142457981839]
  3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis.<n>Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding.<n>Key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions.<n>We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives.
  arXiv  Detail & Related papers  (2025-12-31T10:20:01Z)
• SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation [114.57192386025373]
  SegSplat is a novel framework designed to bridge the gap between rapid, feed-forward 3D reconstruction and rich, open-vocabulary semantic understanding.<n>This work represents a significant step towards practical, on-the-fly generation of semantically aware 3D environments.
  arXiv  Detail & Related papers  (2025-11-23T10:26:38Z)
• COB-GS: Clear Object Boundaries in 3DGS Segmentation Based on Boundary-Adaptive Gaussian Splitting [67.03992455145325]
  3D segmentation based on 3D Gaussian Splatting (3DGS) struggles with accurately delineating object boundaries.<n>We introduce Clear Object Boundaries for 3DGS (COB-GS), which aims to improve segmentation accuracy.<n>For semantic guidance, we introduce a boundary-adaptive Gaussian splitting technique.<n>For the visual optimization, we rectify the degraded texture of the 3DGS scene.
  arXiv  Detail & Related papers  (2025-03-25T08:31:43Z)
• InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception [17.530797215534456]
  3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality.<n>We propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances.<n>Our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation.
  arXiv  Detail & Related papers  (2024-11-28T16:08:36Z)
• Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision [9.96433151449016]
  This paper introduces a novel approach to 3D semantic segmentation, distinguished by incorporating a hybrid blend of 2D and 3D computer vision techniques. We conduct 2D semantic segmentation on RGB images linked to 3D point clouds and extend the results to 3D using an extrusion technique for specific class labels. This model serves as the current state-of-the-art 3D semantic segmentation model on the KITTI-360 dataset.
  arXiv  Detail & Related papers  (2024-07-23T00:04:10Z)
• SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition [66.56357905500512]
  3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis.<n>We propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS.<n>Our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.
  arXiv  Detail & Related papers  (2024-01-31T14:19:03Z)
• Segment Any 3D Gaussians [85.93694310363325]
  This paper presents SAGA, a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS)<n>Given 2D visual prompts as input, SAGA can segment the corresponding 3D target represented by 3D Gaussians within 4 ms.<n>We show that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-01T17:15:24Z)
• SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings [12.79344668998054]
  We propose SwIPE (Segmentation with Implicit Patch Embeddings) to enable accurate local boundary delineation and global shape coherence. We show that SwIPE significantly improves over recent implicit approaches and outperforms state-of-the-art discrete methods with over 10x fewer parameters.
  arXiv  Detail & Related papers  (2023-07-23T20:55:11Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)

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.