  In this paper, we study the problem of 3D reconstruction from a single-view
RGB image and propose a novel approach called DIG3D for 3D object
reconstruction and novel view synthesis. Our method utilizes an encoder-decoder
framework which generates 3D Gaussians in decoder with the guidance of
depth-aware image features from encoder. In particular, we introduce the use of
deformable transformer, allowing efficient and effective decoding through 3D
reference point and multi-layer refinement adaptations. By harnessing the
benefits of 3D Gaussians, our approach offers an efficient and accurate
solution for 3D reconstruction from single-view images. We evaluate our method
on the ShapeNet SRN dataset, getting PSNR of 24.21 and 24.98 in car and chair
dataset, respectively. The result outperforming the recent method by around
2.25%, demonstrating the effectiveness of our method in achieving superior
results.
