9 accepted papers
Despite recent advances, Vision Language Models (VLMs) still struggle to grasp the dynamics of the world. We note that the ability to reason about a 4D scene, challenging in itself, is further complicated by two factors. First, VLMs observe motion indirectly via its projection onto 2D images. Second
Conventional monocular depth estimators suffer from visual ambiguities and nuisances. We demonstrate that language can improve the fidelity of estimates by providing additional information through text as a condition, thereby reducing the solution space for depth estimates. This conditional distribu
We propose a method of adapting pretrained depth completion models to test time data in an unsupervised manner. Depth completion models are (pre)trained to produce dense depth maps from pairs of RGB image and sparse depth maps in ideal capture conditions (source domain), e.g., well-illuminated, high…
We present ProtoDepth, a novel prototype-based approach for continual learning of unsupervised depth completion, the multimodal 3D reconstruction task of predicting dense depth maps from RGB images and sparse point clouds. The unsupervised learning paradigm is well-suited for continual learning, as…
The ability to associate touch with other modalities has huge implications for humans and computational systems. However multimodal learning with touch remains challenging due to the expensive data collection process and non-standardized sensor outputs. We introduce UniTouch a unified tactile model…
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection during the image formation process. Any scale chosen is a bias, typically stemming from training on a dataset; hence, exis…
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguou…
Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classification task. In this paper, we first collaborate CLIP and GPT to be a unified 3D…
Current audio-visual separation methods share a standard architecture design where an audio encoder-decoder network is fused with visual encoding features at the encoder bottleneck. This design confounds the learning of multi-modal feature encoding with robust sound decoding for audio separation. To…