Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions (2412.16467v1)

Abstract: It is vital to recover 3D geometry from multi-view RGB images in many 3D computer vision tasks. The latest methods infer the geometry represented as a signed distance field by minimizing the rendering error on the field through volume rendering. However, it is still challenging to explicitly impose constraints on surfaces for inferring more geometry details due to the limited ability of sensing surfaces in volume rendering. To resolve this problem, we introduce a method to infer signed distance functions (SDFs) with a better sense of surfaces through volume rendering. Using the gradients and signed distances, we establish a small surface patch centered at the estimated intersection along a ray by pulling points randomly sampled nearby. Hence, we are able to explicitly impose surface constraints on the sensed surface patch, such as multi-view photo consistency and supervision from depth or normal priors, through volume rendering. We evaluate our method by numerical and visual comparisons on scene benchmarks. Our superiority over the latest methods justifies our effectiveness.

• The paper introduces a method for sensing surface patches on the zero level set of a signed distance function (SDF) during volume rendering to improve 3D geometry reconstruction.
• The approach estimates surface patches by using predicted signed distances and gradients to project sampled points onto the surface, creating denser constraints than single intersection points.
• Evaluations on datasets like ScanNet and Replica show improved accuracy, completeness, and visual fidelity compared to state-of-the-art methods, reducing artifacts and maintaining surface continuity.

Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions

The paper "Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions" by Sijia Jiang et al. presents a method aiming to improve the reconstruction of 3D geometry from multi-view RGB images, specifically addressing the challenge of inferring high-fidelity signed distance functions (SDFs) via volume rendering. This approach is driven by the goal of enhancing surface detail recovery and mitigating common artifacts encountered during traditional rendering processes.

Methodological Approach

Traditional methodologies for reconstructing 3D surfaces through neural implicit representations often face limitations due to an inadequate perception of surface boundaries. The primary innovation introduced in this work is the concept of sensing surface patches, which facilitates more explicit surface constraints during the volume rendering process. The central strategy revolves around leveraging predicted signed distances and their gradients to estimate a surface patch on the zero level set of the SDF. This is done by mathematically "pulling" randomly sampled points onto the surface, thereby creating a denser representation compared to single intersection points typically used in existing methods.

Technique and Implementation

The authors incorporate both differential rendering and explicit surface constraints through:

1. Enhanced Volume Rendering: The authors utilize predicted signed distances and their gradients to render images while imposing additional constraints that traditional volume rendering lacks.
2. Surface Patch Sensing: For each ray, instead of finding a single intersection point on the zero level set, a patch is formed by projecting surrounding sampled points onto the actual surface, enhancing the model's awareness of the geometry.
3. Hybrid Loss Functions: By integrating volumetric constraints (e.g., RGB, depth consistency) with surface-specific terms (e.g., photometric and geometric fitting losses), the approach optimizes both the density field and surface detail.

Evaluation and Results

The proposed methodology undergoes rigorous evaluation against state-of-the-art methods on datasets like ScanNet and Replica. The results are quantified through standard metrics such as accuracy, completeness, and Chamfer Distance (CD). Notably, the numerical results showcase enhanced precision and recall rates, accompanied by visually improved reconstructions that better adhere to the underlying ground truth surfaces, offering superior fidelity in smooth and complex regions alike. Visual comparisons further substantiate the method's capability to reduce artifacts and maintain surface continuity more effectively than existing techniques.

Implications and Future Directions

The implications of this research traverse practical applications in 3D model reconstruction, AR/VR simulations, and robotics, where accurate scene understanding is crucial. Theoretically, this work underscores the importance of considering refined geometric cues in enhancing neural 3D representations.

For future endeavors, the incorporation of learning frameworks that dynamically adapt the variance used in the Gaussian sampling of surface patches could be explored. Furthermore, integrating this method with real-time rendering systems could lead to advancements in generating dynamic scene graphics.

This paper offers significant strides in rectifying the limitations of traditional SDF reconstruction methods via a nuanced approach to surface constraint application, paving the way for advancements in responsive and detailed 3D scene reconstructions.