EvaSurf: Effcient View-Aware Implicit Textured Surface Reconstruction
on Mobile Devices

Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency constraints or learned features, while differentiable rendering methods like Neural Radiance Fields (NeRF) use differentiable volume rendering or surface-based representation to generate high-fidelity scenes. However, these methods require excessive runtime for rendering, making them impractical for daily applications. To address these challenges, we present EvaSurf, an Efficient View-Aware implicit textured Surface reconstruction method on mobile devices. In our method, we first employ an efficient surface-based model with a multi-view supervision module to ensure accurate mesh reconstruction. To enable high-fidelity rendering, we learn an implicit texture embedded with view-aware encoding to capture view-dependent information. Furthermore, with the explicit geometry and the implicit texture, we can employ a lightweight neural shader to reduce the expense of computation and further support real-time rendering on common mobile devices. Extensive experiments demonstrate that our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets. Moreover, our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second), with a final package required for rendering taking up only 40-50 MB.

We design a two-stage framework to respectively reconstruct the geometry and appearance. (1) In the first stage, we employ an efficient surface-based model with a multi-view supervision module to learn accurate structures of geometry. (2) In the second stage of appearance reconstruction, the view-dependent effect severely influences the quality of rendering images. Therefore, we incorporate view-aware encoding into an implicit texture, to assign different weights to the dimensions of the view-aware texture, thus modeling the appearance with view-dependent effects. (3) With the explicit geometry and the learned implicit texture, we show that a lightweight neural shader is sufficient to achieve high-quality differentiable rendering. The small size of the neural shader can greatly reduce the consumption of computation and memory sources, empowering real-time rendering on mobile devices.

To demonstrate the effectiveness of our method, we also construct experiments on a set of real-world high-resolution (2K) data. This dataset comprises 15 objects, with each object consisting of over 200 images. These images are captured by accurate camera poses, specifically, we provide 3 types of camera poses for each object following the convention of Colmap (Sparse), NeRF (transforms.json) and Neus (cameras_sphere.npz). The corresponding masks for the images are also included.

The reconstructed results are shown below. The dataset would be released soon.