Problem
Research questions and friction points this paper is trying to address.
contrastive learning
3D spatial awareness
2D representations
scene understanding
depth
Depth-Guided Contrastive Learning for 2D Representations with 3D Spatial Awareness
🤖 AI Summary
本文提出Depth-Guided Contrastive Learning方法，通过利用深度信息增强2D图像表示中的3D空间感知能力，从而提高场景理解。
📝 Abstract
Standard contrastive learning frameworks are mainly designed from a semantic perspective, yet learning 2D visual representations that preserve 3D spatial structure is also important for scene understanding. In this work, we propose Depth-Guided Contrastive Learning (DGCL), a simple auxiliary objective that injects 3D spatial awareness into 2D contrastive representation learning. Our key idea is to use depth to convert local 3D proximity into contrastive similarity: pixels that are closer in 3D space are encouraged to have more similar representations than pixels that are farther apart. Instead of relying on absolute depth values, DGCL formulates supervision through relative 3D distance comparisons among randomly sampled pixels, making the objective invariant to depth scale, efficient to compute, and easy to integrate into existing contrastive frameworks. Experiments across different datasets and models show that DGCL consistently improves 2D representation learning and benefits semantic downstream tasks by stronger spatial and geometric understanding. The code is available on https://github.com/LeungTsang/DGCL.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Depth-Guided Contrastive Learning
3D Spatial Awareness
Relative 3D Distance Comparisons
🔎 Similar Papers
No similar papers found.
