3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers

3D reconstruction aims to reconstruct 3D objects from 2D views. Previous works for 3D reconstruction mainly focus on feature matching between views or using CNNs as backbones. Recently, Transformers have been shown effective in multiple applications of computer vision. However, whether or not Transformers can be used for 3D reconstruction is still unclear. In this paper, we fill this gap by proposing 3D-RETR, which is able to perform end-to-end 3D REconstruction with TRansformers. 3D-RETR first uses a pretrained Transformer to extract visual features from 2D input images. 3D-RETR then uses another Transformer Decoder to obtain the voxel features. A CNN Decoder then takes as input the voxel features to obtain the reconstructed objects. 3D-RETR is capable of 3D reconstruction from a single view or multiple views. Experimental results on two datasets show that 3DRETR reaches state-of-the-art performance on 3D reconstruction. Additional ablation study also demonstrates that 3D-DETR benefits from using Transformers.

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We extend the popular BART architecture to a multi-modal model. We design a new pretraining task to improve the model performance on Visual Commonsense Generation task. Our pretraining task improves the Visual Commonsense Generation performance by leveraging knowledge from a large language model pretrained on an external knowledge graph. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on Visual Commonsense Generation. Experimental results show that by pretraining, our model reaches state-of-the-art performance on the Visual Commonsense Generation task.