For 3D object manipulation, methods that build an explicit 3D representation perform better
than those relying only on camera images. But using explicit 3D representations like voxels …

Performing 3D dense captioning and visual grounding requires a common and shared
understanding of the underlying multimodal relationships. However, despite some previous …

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by
using a joint prediction from a conventional 3D model and a well-pretrained 2D model …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

In recent years, the field of 3D self-supervised learning has witnessed significant progress,
resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that …

Neural radiance fields (NeRFs) generally require many images with accurate poses for
accurate novel view synthesis, which does not reflect realistic setups where views can be …

Pure Transformer models have achieved impressive success in natural language
processing and computer vision. However, one limitation with Transformers is their need for …

With the recent advances in video and 3D understanding, novel 4D spatio-temporal
methods fusing both concepts have emerged. Towards this direction, the Ego4D Episodic …

Contrastive learning has emerged as a promising paradigm for 3D open-world
understanding ie aligning point cloud representation to image and text embedding space …

Multi-view projection techniques have shown themselves to be highly effective in achieving
top-performing results in the recognition of 3D shapes. These methods involve learning how …