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

TL;DR

A novel perceptual-prior-guided 3D scene representation and panoptic understanding method is introduced, which reformulates panoptic understanding within neural radiance fields as a linear assignment problem involving 2D semantics and instance recognition.

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.

Figures (8)

• Figure 1: Illustration of 3D Scene Representation and Panoptic Understanding
• Figure 2: The Categories of Segmentation in Scene Understanding
• Figure 3: Overview of the Perceptual Prior Guided 3D Scene Representation and Panoptic Understanding. By employing an understanding feature grid and dual decoders, we jointly learn geometric, appearance, semantic, and instance information. Perceptual prior guidance further enhances the alignment between geometric and semantic features, enabling accurate and consistent 3D panoptic segmentation from arbitrary viewpoints.
• Figure 4: Illustration of Observation Data Preprocessing. This process involves calibrating the intrinsic and extrinsic parameters of the visual sensor and generating semantic and instance supervision maps using a pre-trained panoptic segmentation network.
• Figure 5: Overview of the implicit scene representation and understanding model $\mathcal{S}$. Geometric and understanding feature grids are integrated, and multi-layer perceptrons are employed to generate semantic and instance probability distributions. The geometry, appearance, semantic, and instance decoders are jointly trained to predict volume density, directional color, semantic and instance probability distributions.