We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to
produce metric predictions. Even so, the resulting models will be geometry-specific, with …

Recent implicit neural representations have shown great results for novel view synthesis.
However, existing methods require expensive per-scene optimization from many views …

Depth-aware panoptic segmentation is an emerging topic in computer vision which
combines semantic and geometric understanding for more robust scene interpretation …

Managing the dynamic regions in the photometric loss formulation has been a main issue for
handling the self-supervised depth estimation problem. Most previous methods have …

The common trade-offs of state-of-the-art methods for multi-shape representation (a single
model" packing" multiple objects) involve trading modeling accuracy against memory and …

Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel
view synthesis. However, standard volume rendering approaches struggle with degenerate …

Recent advances in deep learning have led to a data-centric intelligence ie artificially
intelligent models unlocking the potential to ingest a large amount of data and be really …

In this paper we propose the Ray-Patch querying, a novel model to efficiently query
transformers to decode implicit representations into target views. Our Ray-Patch decoding …

We present WildHands, a method for 3D hand pose estimation in egocentric images in the
wild. This is challenging due to (a) lack of 3D hand pose annotations for images in the wild …