HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting

• URL: http://arxiv.org/abs/2403.12722v1
• Date: Tue, 19 Mar 2024 13:39:05 GMT
• Title: HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting
• Authors: Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, Yiyi Liao,
• Abstract summary: holistic understanding of urban scenes based on RGB images is a challenging yet important problem. Our main idea involves the joint optimization of geometry, appearance, semantics, and motion using a combination of static and dynamic 3D Gaussians. Our approach offers the ability to render new viewpoints in real-time, yielding 2D and 3D semantic information with high accuracy.
• Score: 53.6394928681237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Holistic understanding of urban scenes based on RGB images is a challenging yet important problem. It encompasses understanding both the geometry and appearance to enable novel view synthesis, parsing semantic labels, and tracking moving objects. Despite considerable progress, existing approaches often focus on specific aspects of this task and require additional inputs such as LiDAR scans or manually annotated 3D bounding boxes. In this paper, we introduce a novel pipeline that utilizes 3D Gaussian Splatting for holistic urban scene understanding. Our main idea involves the joint optimization of geometry, appearance, semantics, and motion using a combination of static and dynamic 3D Gaussians, where moving object poses are regularized via physical constraints. Our approach offers the ability to render new viewpoints in real-time, yielding 2D and 3D semantic information with high accuracy, and reconstruct dynamic scenes, even in scenarios where 3D bounding box detection are highly noisy. Experimental results on KITTI, KITTI-360, and Virtual KITTI 2 demonstrate the effectiveness of our approach.

Related papers

• Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering [57.895846642868904]
  We present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning. voxelization infers per-object occupancy probabilities at individual spatial locations. Our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids.
  arXiv  Detail & Related papers  (2024-07-30T15:33:58Z)
• DGD: Dynamic 3D Gaussians Distillation [14.7298711927857]
  We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene. We present DGD, a unified 3D representation for both the appearance and semantics of a dynamic 3D scene.
  arXiv  Detail & Related papers  (2024-05-29T17:52:22Z)
• HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2 [1.1874952582465603]
  We leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloLens sensor data, which bypasses the need for pre-processing steps. We evaluate our approach on two self-captured scenes: An outdoor scene of a cultural heritage statue and an indoor scene of a fine-structured plant.
  arXiv  Detail & Related papers  (2024-05-03T11:08:04Z)
• Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting [27.974762304763694]
  We introduce Semantic Gaussians, a novel open-vocabulary scene understanding approach based on 3D Gaussian Splatting. Unlike existing methods, we design a versatile projection approach that maps various 2D semantic features into a novel semantic component of 3D Gaussians. We build a 3D semantic network that directly predicts the semantic component from raw 3D Gaussians for fast inference.
  arXiv  Detail & Related papers  (2024-03-22T21:28:19Z)
• SceneWiz3D: Towards Text-guided 3D Scene Composition [134.71933134180782]
  Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. We introduce SceneWiz3D, a novel approach to synthesize high-fidelity 3D scenes from text.
  arXiv  Detail & Related papers  (2023-12-13T18:59:30Z)
• NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization [80.3424839706698]
  We present NeurOCS, a framework that uses instance masks 3D boxes as input to learn 3D object shapes by means of differentiable rendering. Our approach rests on insights in learning a category-level shape prior directly from real driving scenes. We make critical design choices to learn object coordinates more effectively from an object-centric view.
  arXiv  Detail & Related papers  (2023-05-28T16:18:41Z)
• Generating Visual Spatial Description via Holistic 3D Scene Understanding [88.99773815159345]
  Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images. With an external 3D scene extractor, we obtain the 3D objects and scene features for input images. We construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
  arXiv  Detail & Related papers  (2023-05-19T15:53:56Z)
• SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [98.74706005223685]
  3D scene understanding plays a vital role in vision-based autonomous driving. We propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.
  arXiv  Detail & Related papers  (2023-03-16T17:59:08Z)

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.