EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding

TL;DR

EmbodiedSplat is proposed, an online feed-forward 3DGS for open-vocabulary scene understanding that enables simultaneous online 3D reconstruction and 3D semantic understanding from the streaming images.

Abstract

Understanding a 3D scene immediately with its exploration is essential for embodied tasks, where an agent must construct and comprehend the 3D scene in an online and nearly real-time manner. In this study, we propose EmbodiedSplat, an online feed-forward 3DGS for open-vocabulary scene understanding that enables simultaneous online 3D reconstruction and 3D semantic understanding from the streaming images. Unlike existing open-vocabulary 3DGS methods which are typically restricted to either offline or per-scene optimization setting, our objectives are two-fold: 1) Reconstructs the semantic-embedded 3DGS of the entire scene from over 300 streaming images in an online manner. 2) Highly generalizable to novel scenes with feed-forward design and supports nearly real-time 3D semantic reconstruction when combined with real-time 2D models. To achieve these objectives, we propose an Online Sparse Coefficients Field with a CLIP Global Codebook where it binds the 2D CLIP embeddings to each 3D Gaussian while minimizing memory consumption and preserving the full semantic generalizability of CLIP. Furthermore, we generate 3D geometric-aware CLIP features by aggregating the partial point cloud of 3DGS through 3D U-Net to compensate the 3D geometric prior to 2D-oriented language embeddings. Extensive experiments on diverse indoor datasets, including ScanNet, ScanNet++, and Replica, demonstrate both the effectiveness and efficiency of our method. Check out our project page in https://0nandon.github.io/EmbodiedSplat/.

Figures (12)

• Figure 1: Overall framework of our EmbodiedSplat. We endow feed-forward 3DGS with semantic understanding capabilities by binding the two types of CLIP features: 1) 2D Semantic Features are attached to each Gaussian via Sparse Coefficient Field with CLIP Global Codebook, effectively reducing memory consumption while preserving semantic generalizability of CLIP. 2) 3D Geometric-aware Features are produced by aggregating the feature point cloud of 3DGS through 3D U-Net choy20194d and temporal-aware memory adapter xu2024memory. These two types of features enable mutual compensation between semantic and 3D geometry, which results in superior understanding capabilities compared to the existing baselines.
• Figure 2: Quantitative comparisons on 3D semantic segmentation.
• Figure 3: 2D-rendered object search.
• Figure 4: Online 3D reasoning for class "Bed".
• Figure 5: Depth visualizations with RGB-D inputs.