  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
$\textbf{EvaSurf}$, an $\textbf{E}$fficient $\textbf{V}$iew-$\textbf{A}$ware
implicit textured $\textbf{Surf}$ace 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 a set of
Gaussian lobes 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.
