Normal-guided Detail-Preserving Neural Implicit Functions for High-Fidelity 3D Surface Reconstruction

Neural implicit representations have emerged as a powerful paradigm for 3D reconstruction. However, despite their success, existing methods fail to capture fine geometric details and thin structures, especially in scenarios where only sparse RGB views of the objects of interest are available. We hypothesize that current methods for learning neural implicit representations from RGB or RGBD images produce 3D surfaces with missing parts and details because they only rely on 0-order differential properties, i.e. the 3D surface points and their projections, as supervisory signals. Such properties, however, do not capture the local 3D geometry around the points and also ignore the interactions between points. This paper demonstrates that training neural representations with first-order differential properties, i.e. surface normals, leads to highly accurate 3D surface reconstruction even in situations where only as few as two RGB (front and back) images are available. Given multiview RGB images of an object of interest, we first compute the approximate surface normals in the image space using the gradient of the depth maps produced using an off-the-shelf monocular depth estimator such as Depth Anything model. An implicit surface regressor is then trained using a loss function that enforces the first-order differential properties of the regressed surface to match those estimated from Depth Anything. Our extensive experiments on a wide range of real and synthetic datasets show that the proposed method achieves an unprecedented level of reconstruction accuracy even when using as few as two RGB views. The detailed ablation study also demonstrates that normal-based supervision plays a key role in this significant improvement in performance, enabling the 3D reconstruction of intricate geometric details and thin structures that were previously challenging to capture.

RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation

3D generation has rapidly accelerated in the past decade owing to the progress in the field of generative modeling. Score Distillation Sampling (SDS) based rendering has improved 3D asset generation to a great extent. Further, the recent work of Denoising Diffusion Policy Optimization (DDPO) demonstrates that the diffusion process is compatible with policy gradient methods and has been demonstrated to improve the 2D diffusion models using an aesthetic scoring function. We first show that this aesthetic scorer acts as a strong guide for a variety of SDS-based methods and demonstrates its effectiveness in text-to-3D synthesis. Further, we leverage the DDPO approach to improve the quality of the 3D rendering obtained from 2D diffusion models. Our approach, DDPO3D, employs the policy gradient method in tandem with aesthetic scoring. To the best of our knowledge, this is the first method that extends policy gradient methods to 3D score-based rendering and shows improvement across SDS-based methods such as DreamGaussian, which are currently driving research in text-to-3D synthesis. Our approach is compatible with score distillation-based methods, which would facilitate the integration of diverse reward functions into the generative process. Our project page can be accessed via https://ddpo3d.github.io.