CoCoNets: Continuous Contrastive 3D Scene Representations

TL;DR

This paper introduces CoCoNets, a self-supervised method for learning continuous 3D scene representations from RGB and RGB-D data, enabling improved 3D understanding tasks like object tracking and detection.

Contribution

It presents a novel contrastive learning framework for amodal 3D feature representations that are scalable, occlusion-aware, and effective across various scene understanding tasks.

Findings

Outperforms existing 3D feature learning methods

Enables querying of any 3D location, visible or not

Improves object tracking and detection accuracy

Abstract

This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection. The model infers a latent3D representation of the scene in the form of 3D feature points, where each continuous world 3D point is mapped to its corresponding feature vector. The model is trained for contrastive view prediction by rendering 3D feature clouds in queried viewpoints and matching against the 3D feature point cloud predicted from the query view. Notably, the representation can be queried for any 3D location, even if it is not visible from the input view. Our model brings together three powerful ideas of recent exciting research work: 3D feature grids as a neural bottleneck for view prediction, implicit functions for handling resolution limitations of 3D grids, and contrastive learning for unsupervised training of feature representations. We show the resulting 3D visual feature representations effectively scale across objects and scenes, imagine information occluded or missing from the input viewpoints, track objects over time, align semantically related objects in 3D, and improve 3D object detection. We outperform many existing state-of-the-art methods for 3D feature learning and view prediction, which are either limited by 3D grid spatial resolution, do not attempt to build amodal 3D representations, or do not handle combinatorial scene variability due to their non-convolutional bottlenecks.