Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation (2205.04334v1)

Abstract: We present Panoptic Neural Fields (PNF), an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is represented by an oriented 3D bounding box and a multi-layer perceptron (MLP) that takes position, direction, and time and outputs density and radiance. The background stuff is represented by a similar MLP that additionally outputs semantic labels. Each object MLPs are instance-specific and thus can be smaller and faster than previous object-aware approaches, while still leveraging category-specific priors incorporated via meta-learned initialization. Our model builds a panoptic radiance field representation of any scene from just color images. We use off-the-shelf algorithms to predict camera poses, object tracks, and 2D image semantic segmentations. Then we jointly optimize the MLP weights and bounding box parameters using analysis-by-synthesis with self-supervision from color images and pseudo-supervision from predicted semantic segmentations. During experiments with real-world dynamic scenes, we find that our model can be used effectively for several tasks like novel view synthesis, 2D panoptic segmentation, 3D scene editing, and multiview depth prediction.

• The paper introduces a unified MLP approach that decomposes dynamic 3D scenes into object-specific and background segments.
• It leverages category-specific priors and meta-learned initialization to achieve compact, efficient neural representations.
• Experiments on KITTI datasets demonstrate state-of-the-art performance in novel view synthesis, panoptic segmentation, and 3D scene editing.

Introduction

In their recent work, "Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation," a team of researchers from Google Research and several prestigious universities presented an innovative approach to neural scene representation using machine learning. The proposed method, called Panoptic Neural Fields (PNF), effectively captures the nuances of a dynamic 3D environment, distinguishing between discrete objects (referred to as "things") and the surrounding environment (referred to as "stuff") with notable finesse. Leveraging neural networks, particularly Multi-Layer Perceptrons (MLPs), the model handles complex, moving scenes by training on RGB images alone.

Panoptic Neural Fields

PNF operates by decomposing a dynamic scene into an assembly of MLPs, each corresponding to a distinct object within the 3D space. What sets PNF apart from previous methods is its unique compositionality: every "thing" is represented by its 3D bounding box and an MLP that produces density and radiance values. The "stuff" category, composing the scene's background, is encapsulated by a separate MLP that additionally outputs semantic labels.

By engineering an object-aware MLP architecture, PNF can bypass the limitations inherent in previous scene representations that were not only object-agnostic but also lacked semantic understanding. These advancements are largely propelled by category-specific priors implemented through a meta-learned initialization strategy, leading to more compact and efficient MLPs.

Evaluation and Contributions

Comprehensive experiments were carried out on the KITTI and KITTI-360 datasets to evaluate the model's proficiency in tasks, including but not restricted to novel view synthesis, panoptic segmentation, and 3D scene editing. Empirically, the PNF model delivered state-of-the-art performance in reconstructing dynamic scenes, matching and exceeding the panorama quality level of several benchmarks.

The researchers highlighted several key contributions:

1. Introduction of a pioneering method that can infer a panoptic-radiance field from mere image data, distinguishing between dynamic "things" and static "stuff."
2. Achievement of state-of-the-art results across multiple tasks and datasets by leveraging a unified model.
3. Implementation of category-specific shape and appearance priors via meta-learned initialization, leading to smaller and faster MLPs than prior object-aware models.
4. Joint optimization of neural fields and object poses, which adapts to noisy object poses and image segmentations.

Methodology

PNF's training involves using off-the-shelf algorithms to predict camera parameters, object tracks, and 2D image segments for all images in a dataset. Following that, the method employs self-supervised optimization from color images and pseudo-supervision from predicted segmentations to fine-tune MLP weights and bounding box parameters. This is substantially different from shared MLP approaches, marking a significant departure in how dynamic object instances are represented.

Future Work and Societal Impact

Despite the method's computational intensity, which limits its current usage to offline applications, there's optimism for future improvements to mitigate these constraints. Moreover, consideration around potential negative use cases, such as its misuse in surveillance, fabrication of synthetic imagery, or alteration of real imagery, underlines the ethical dimension evident in this domain of AI research.

Conclusion

The paper's findings open a promising pathway in the quest for full 3D scene understanding. The clarity in disentangling and representing dynamic objects and their environment heralds a significant stride for applications ranging from autonomous driving to virtual reality, mapping, and beyond. The intricate balance of precision, efficiency, and category-specific insight established by PNF could influence the future trajectory of how machines perceive and interpret our world in three dimensions.