In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding

• URL: http://arxiv.org/abs/2410.04529v1
• Date: Sun, 6 Oct 2024 15:49:58 GMT
• Title: In-Place Panoptic Radiance Field Segmentation with Perceptual Prior for 3D Scene Understanding
• Authors: Shenghao Li,
• Abstract summary: This paper introduces a novel perceptual-prior-guided 3D scene representation and panoptic understanding method. It reformulates panoptic understanding within neural radiance fields as a linear assignment problem involving 2D semantics and instance recognition. Experiments and ablation studies under challenging conditions, including synthetic and real-world scenes, demonstrate the proposed method's effectiveness.
• Score: 1.8130068086063336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 3D scene representation and panoptic understanding are essential for applications such as virtual reality, robotics, and autonomous driving. However, challenges persist with existing methods, including precise 2D-to-3D mapping, handling complex scene characteristics like boundary ambiguity and varying scales, and mitigating noise in panoptic pseudo-labels. This paper introduces a novel perceptual-prior-guided 3D scene representation and panoptic understanding method, which reformulates panoptic understanding within neural radiance fields as a linear assignment problem involving 2D semantics and instance recognition. Perceptual information from pre-trained 2D panoptic segmentation models is incorporated as prior guidance, thereby synchronizing the learning processes of appearance, geometry, and panoptic understanding within neural radiance fields. An implicit scene representation and understanding model is developed to enhance generalization across indoor and outdoor scenes by extending the scale-encoded cascaded grids within a reparameterized domain distillation framework. This model effectively manages complex scene attributes and generates 3D-consistent scene representations and panoptic understanding outcomes for various scenes. Experiments and ablation studies under challenging conditions, including synthetic and real-world scenes, demonstrate the proposed method's effectiveness in enhancing 3D scene representation and panoptic segmentation accuracy.

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)
• SUGAR: Pre-training 3D Visual Representations for Robotics [85.55534363501131]
  We introduce a novel 3D pre-training framework for robotics named SUGAR. SUGAR captures semantic, geometric and affordance properties of objects through 3D point clouds. We show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.
  arXiv  Detail & Related papers  (2024-04-01T21:23:03Z)
• Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review [26.436253160392123]
  Review thoroughly examines the role of semantically-aware Neural Radiance Fields (NeRFs) in visual scene understanding. NeRFs adeptly infer 3D representations for both stationary and dynamic objects in a scene.
  arXiv  Detail & Related papers  (2024-02-17T00:15:09Z)
• SERF: Fine-Grained Interactive 3D Segmentation and Editing with Radiance Fields [92.14328581392633]
  We introduce a novel fine-grained interactive 3D segmentation and editing algorithm with radiance fields, which we refer to as SERF. Our method entails creating a neural mesh representation by integrating multi-view algorithms with pre-trained 2D models. Building upon this representation, we introduce a novel surface rendering technique that preserves local information and is robust to deformation.
  arXiv  Detail & Related papers  (2023-12-26T02:50:42Z)
• Scene-Generalizable Interactive Segmentation of Radiance Fields [64.37093918762]
  We make the first attempt at Scene-Generalizable Interactive in Radiance Fields (SGISRF) We propose a novel SGISRF method, which can perform 3D object segmentation for novel (unseen) scenes represented by radiance fields, guided by only a few interactive user clicks in a given set of multi-view 2D images. Experiments on two real-world challenging benchmarks covering diverse scenes demonstrate 1) effectiveness and scene-generalizability of the proposed method, 2) favorable performance compared to classical method requiring scene-specific optimization.
  arXiv  Detail & Related papers  (2023-08-09T17:55:50Z)
• SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections [49.802462165826554]
  We present SceneDreamer, an unconditional generative model for unbounded 3D scenes. Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations.
  arXiv  Detail & Related papers  (2023-02-02T18:59:16Z)
• Panoptic Lifting for 3D Scene Understanding with Neural Fields [32.59498558663363]
  We propose a novel approach for learning panoptic 3D representations from images of in-the-wild scenes. Our method requires only machine-generated 2D panoptic segmentation masks inferred from a pre-trained network. Experimental results validate our approach on the challenging Hypersim, Replica, and ScanNet datasets.
  arXiv  Detail & Related papers  (2022-12-19T19:15:36Z)
• Neural Groundplans: Persistent Neural Scene Representations from a Single Image [90.04272671464238]
  We present a method to map 2D image observations of a scene to a persistent 3D scene representation. We propose conditional neural groundplans as persistent and memory-efficient scene representations.
  arXiv  Detail & Related papers  (2022-07-22T17:41:24Z)

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.