Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation (2411.14384v2)

Abstract: Existing feed-forward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric prompt images. In this paper, we propose a novel single-stage 3D diffusion model, DiffusionGS, for object and scene generation from a single view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to enforce view consistency and allow the model to generate robustly given prompt views of any directions, beyond object-centric inputs. Plus, to improve the capability and generalization ability of DiffusionGS, we scale up 3D training data by developing a scene-object mixed training strategy. Experiments show that our method enjoys better generation quality (2.20 dB higher in PSNR and 23.25 lower in FID) and over 5x faster speed (~6s on an A100 GPU) than SOTA methods. The user study and text-to-3D applications also reveals the practical values of our method. Our Project page at https://caiyuanhao1998.github.io/project/DiffusionGS/ shows the video and interactive generation results.

• The paper introduces DiffusionGS, a single-stage 3D diffusion model that integrates Gaussian splatting into the denoising process for robust 3D consistency.
• The paper achieves state-of-the-art results by exceeding prior methods by 2.2 dB in PSNR and generating images in about 6 seconds on an A100 GPU.
• The paper employs a scene-object mixed training strategy and RPPC camera conditioning to enhance generalization and improve 3D representation fidelity.

Overview of "Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation"

The paper presents a novel approach to address the challenges involved in generating 3D representations from single-view images, a task with significant implications across a variety of fields, from augmented reality to robotics. The authors propose a single-stage 3D diffusion model named DiffusionGS, which leverages Gaussian splatting within the diffusion denoising framework. This technique ensures 3D consistency through the direct output of 3D Gaussian point clouds at each diffusion timestep. The research addresses critical limitations in existing methodologies, notably the inability of prior models to maintain consistency across varying viewpoints and the tendency to focus predominantly on object-centric scenes.

Key Contributions

The paper introduces several innovations:

1. Single-Stage 3D Diffusion Model: DiffusionGS integrates 3D Gaussian splatting directly into the diffusion denoiser. Unlike two-stage methods that separately handle view generation and 3D reconstruction, this approach ensures holistic 3D consistency and robust performance across diverse input perspectives.
2. Scalable Object and Scene Generation: The model demonstrates a significant improvement in both quality and speed. Experiments indicate that DiffusionGS achieves superior performance—exceeding state-of-the-art methods by 2.2 dB in PSNR for objects and scenes, and offering a faster generation speed of approximately 6 seconds on an A100 GPU.
3. Scene-Object Mixed Training Strategy: To enhance the model's generalization and capability, the authors introduce a mixed training strategy that amalgamates 3D scene and object data. This approach successfully addresses training instability issues encountered due to domain discrepancies between different datasets.
4. Reference-Point Pl\"ucker Coordinate (RPPC): The paper suggests an improved camera pose conditioning method to better capture depth and geometry information, enhancing the 3D representation's fidelity.

Experimental Results

The empirical evaluations affirm DiffusionGS's efficiency and efficacy. On benchmark datasets, the proposed methodology not only improves quantifiable metrics such as PSNR and FID but also results in visually superior outputs when compared to existing methods, as verified by the user paper. The model's robustness is highlighted through its ability to manage complex geometries and varied texture challenges that are common in both objects and entire scenes.

Implications and Future Prospects

The integration of Gaussian splatting into a diffusion framework represents a significant step forward in the domain of single-view-to-3D generation, potentially reshaping techniques used in digital media creation and interactive applications. The advancements in speed and consistency also pave the way for more practical applications in real-time settings, including virtual reality and gaming.

Future research could explore further optimization of Gaussian splats for even faster performance and adaptability to higher resolutions or more diverse scene types. Moreover, integrating this approach with advancements in neural radiance fields could open up new avenues for understanding and navigating 3D spaces with increased precision.

In conclusion, this paper makes substantial contributions to the field of 3D generation from images by proposing a cohesive, efficient, and high-fidelity model. The research not only enhances existing methodologies but also sets a benchmark for future studies, driving advancements in both theoretical and practical applications of AI in 3D scene understanding.