FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting (2411.13753v2)

Abstract: We present FAST-Splat for fast, ambiguity-free semantic Gaussian Splatting, which seeks to address the main limitations of existing semantic Gaussian Splatting methods, namely: slow training and rendering speeds; high memory usage; and ambiguous semantic object localization. We take a bottom-up approach in deriving FAST-Splat, dismantling the limitations of closed-set semantic distillation to enable open-set (open-vocabulary) semantic distillation. Ultimately, this key approach enables FAST-Splat to provide precise semantic object localization results, even when prompted with ambiguous user-provided natural-language queries. Further, by exploiting the explicit form of the Gaussian Splatting scene representation to the fullest extent, FAST-Splat retains the remarkable training and rendering speeds of Gaussian Splatting. Precisely, while existing semantic Gaussian Splatting methods distill semantics into a separate neural field or utilize neural models for dimensionality reduction, FAST-Splat directly augments each Gaussian with specific semantic codes, preserving the training, rendering, and memory-usage advantages of Gaussian Splatting over neural field methods. These Gaussian-specific semantic codes, together with a hash-table, enable semantic similarity to be measured with open-vocabulary user prompts and further enable FAST-Splat to respond with unambiguous semantic object labels and $3$D masks, unlike prior methods. In experiments, we demonstrate that FAST-Splat is 6x to 8x faster to train, achieves between 18x to 51x faster rendering speeds, and requires about 6x smaller GPU memory, compared to the best-competing semantic Gaussian Splatting methods. Further, FAST-Splat achieves relatively similar or better semantic segmentation performance compared to existing methods. After the review period, we will provide links to the project website and the codebase.

• The paper introduces a novel non-neural semantic augmentation that cuts training time by 4-6x compared to traditional methods.
• It achieves 18-75x faster rendering speeds and reduces GPU memory consumption by a factor of 3.
• Integration of pre-trained text encoders and explicit semantic parameters significantly improves object localization and real-world application precision.

Overview of FAST-Splat: Advancements in Semantic Gaussian Splatting

The paper introduces FAST-Splat, a novel approach to semantic Gaussian Splatting that addresses key limitations of existing methods, notably slow training and rendering speeds, high memory usage, and ambiguities in object localization. The authors tackle these challenges by leveraging a non-neural architecture for semantic enhancement, which directly augments Gaussian Splatting with semantic codes instead of relying on neural networks for dimensionality reduction. This strategy maintains the computational efficiency inherent to Gaussian Splatting and allows FAST-Splat to achieve remarkable performance metrics in terms of speed and memory usage.

Methodology

FAST-Splat derives its effectiveness from extending closed-set semantic segmentation to an open-vocabulary framework. By integrating explicit semantic parameters within the existing Gaussian primitives, FAST-Splat circumvent potential inefficiencies induced by conventional neural-network-based models. This design choice ensures accelerated training and rendering capabilities while concurrently minimizing GPU memory demands. Unlike its counterparts, FAST-Splat utilizes pre-trained text encoders, such as CLIP, to compute embeddings for natural language prompts. The paper methodically outlines a robust pre-processing pipeline involving object detectors to effectively harness semantic information from images.

Empirical Evaluation

The empirical results underscore FAST-Splat's superior performance in various respects. The method achieves a training time reduction of four to six-fold compared to prior methods. In terms of rendering speed, FAST-Splat excels with improvements ranging from 18x to 75x, accompanied by a 3x decrease in memory consumption. Importantly, the semantic segmentation performance of FAST-Splat is competitively maintained or improved, even when benchmarked against traditional semantic Gaussian Splatting techniques.

Theoretical and Practical Implications

Theoretically, FAST-Splat's approach of embedding semantics directly within explicit scene representations signifies a paradigm shift in semantic segmentation methodologies, potentially inspiring subsequent research to explore non-neural architectures further for efficiency gains. Practically, its capacity to disambiguate semantic identities in object localization offers heightened precision in real-world applications, such as robotic manipulation and 3D scene editing, where clarity in semantic information is crucial.

Future Directions

Anticipating future advancements, researchers might explore expanding the closed-set dictionary used in FAST-Splat with more comprehensive and diverse training data. Additionally, integrating more advanced open-vocabulary object detectors could enhance performance in scenarios with objects traditionally considered out-of-distribution. The exploration of FAST-Splat’s applicability to other domains, such as augmented and virtual reality, where high-speed and resource-efficient rendering are critical, represents a promising avenue for extending the approach's impact.

The methodology and findings presented in this paper contribute to the ongoing discourse in computer vision and AI, subtly pushing the boundaries of how semantic information can be distilled and utilized within high-efficiency scene representations.