Uni-Fusion: Universal Continuous Mapping (2303.12678v3)

Abstract: We present Uni-Fusion, a universal continuous mapping framework for surfaces, surface properties (color, infrared, etc.) and more (latent features in CLIP embedding space, etc.). We propose the first universal implicit encoding model that supports encoding of both geometry and different types of properties (RGB, infrared, features, etc.) without requiring any training. Based on this, our framework divides the point cloud into regular grid voxels and generates a latent feature in each voxel to form a Latent Implicit Map (LIM) for geometries and arbitrary properties. Then, by fusing a local LIM frame-wisely into a global LIM, an incremental reconstruction is achieved. Encoded with corresponding types of data, our Latent Implicit Map is capable of generating continuous surfaces, surface property fields, surface feature fields, and all other possible options. To demonstrate the capabilities of our model, we implement three applications: (1) incremental reconstruction for surfaces and color (2) 2D-to-3D transfer of fabricated properties (3) open-vocabulary scene understanding by creating a text CLIP feature field on surfaces. We evaluate Uni-Fusion by comparing it in corresponding applications, from which Uni-Fusion shows high-flexibility in various applications while performing best or being competitive. The project page of Uni-Fusion is available at https://jarrome.github.io/Uni-Fusion/ .

• The paper Uni-Fusion proposes a universal framework for continuous 3D mapping that simultaneously encodes and maps various surface properties and geometries without pre-training.
• Uni-Fusion utilizes a Latent Implicit Map within a voxel grid and Gaussian Process Regression to efficiently encode diverse data into low-dimensional latent vectors for incremental reconstruction.
• The framework enables versatile applications, including accurate surface reconstruction, 2D property transfer, and open-vocabulary scene understanding using CLIP embeddings.

Uni-Fusion: Universal Continuous Mapping Framework

The paper investigates a universal continuous mapping framework known as Uni-Fusion, which introduces an innovative method to simultaneously encode and map various surface properties and geometries in 3D environments. Without relying on any pre-training, Uni-Fusion utilizes a novel universal implicit encoding model, effectively addressing the need for separate models for discrete properties like RGB color, infrared, and other latent features such as CLIP embeddings. This research demonstrates applications across multiple domains, showcasing its versatility and robustness.

Uni-Fusion's architecture is centered around a Latent Implicit Map (LIM) paradigm within a voxel grid, where each voxel contains encoded local information for incremental reconstruction. The encoding uses Gaussian Process Regression (GPR) decoupling, enabling efficient input data processing into low-dimensional latent vectors that can be flexibly combined across various applications. In decoding, features are reconstructed from LIM to reveal respective continuous surfaces and properties, such as color, text-based scene understanding, and even 2D-to-3D property transfers.

Key Contributions

1. Universal Encoder/Decoder Model: Uni-Fusion introduces a universal model for encoding and decoding localized data into voxel-based latent representations without pre-training. It exhibits efficient handling of arbitrary properties using Gaussian Process Regression (GPR) decoupled into low-dimensional vectors.
2. Incremental and Efficient Reconstruction: The framework achieves high accuracy in incremental surface reconstruction using an occupancy and signed distance functions approach. It fuses local LIMs into a coherent global LIM incrementally, which allows real-time surface and property mapping.
3. Open-Vocabulary Scene Understanding: Through leveraging CLIP embeddings, Uni-Fusion can semantically segment and understand scenes from text input without the explicit need for training. This capability enhances potential applications in robotic navigation and interaction with environments through semantic cognition.
4. Diverse Applications with Real-Time Capability: Demonstrated applications include scanning and reconstructing surfaces with accurate color, 2D property transfer into a 3D context, and scene understanding. The framework supports a breadth of uses in fields that require adaptable and efficient perception mechanisms.

Evaluation and Impact

Experiments on notable datasets such as ScanNet, TUM RGB-D, and Replica validate Uni-Fusion's performance in reconstruction quality and processing efficiency. Quantitative comparisons indicate that Uni-Fusion delivers comparable if not superior results to traditional models requiring pre-trained or object-specific encoders. For instance, Uni-Fusion surpasses BNV-Fusion in accuracy and performs exceedingly well when benchmarked against traditional methods for color rendering and surface reconstruction.

From a theoretical perspective, Uni-Fusion presents a framework that could precipitate further research into universal encoders and decoders in robotics and perception systems. Practically, the system's ability to adapt quickly to nuanced environmental data has profound implications for real-time applications such as autonomous robots and augmented reality devices, where adaptability and robustness are paramount.

In summary, Uni-Fusion emerges as a versatile and robust framework that dramatically bridges the gap between diverse mapping applications and real-world data without the extensive overhead typically associated with pre-training or specialized neural network configurations. The continued development and application of this model could significantly enhance the capabilities of AI systems engaged in complex 3D perception tasks.