Towards Unified Representation of Multi-Modal Pre-training for 3D Understanding via Differentiable Rendering

TL;DR

DR-Point introduces a tri-modal pre-training framework that leverages differentiable rendering to unify RGB images, depth images, and 3D point clouds, significantly improving 3D understanding tasks.

Contribution

It presents a novel differentiable rendering-based approach for tri-modal pre-training, enabling effective learning of a unified 3D representation space from limited triplets.

Findings

Outperforms existing self-supervised methods in various 3D tasks

Enhances point cloud reconstruction accuracy

Validates effectiveness through extensive ablation studies

Abstract

State-of-the-art 3D models, which excel in recognition tasks, typically depend on large-scale datasets and well-defined category sets. Recent advances in multi-modal pre-training have demonstrated potential in learning 3D representations by aligning features from 3D shapes with their 2D RGB or depth counterparts. However, these existing frameworks often rely solely on either RGB or depth images, limiting their effectiveness in harnessing a comprehensive range of multi-modal data for 3D applications. To tackle this challenge, we present DR-Point, a tri-modal pre-training framework that learns a unified representation of RGB images, depth images, and 3D point clouds by pre-training with object triplets garnered from each modality. To address the scarcity of such triplets, DR-Point employs differentiable rendering to obtain various depth images. This approach not only augments the supply of depth images but also enhances the accuracy of reconstructed point clouds, thereby promoting the representative learning of the Transformer backbone. Subsequently, using a limited number of synthetically generated triplets, DR-Point effectively learns a 3D representation space that aligns seamlessly with the RGB-Depth image space. Our extensive experiments demonstrate that DR-Point outperforms existing self-supervised learning methods in a wide range of downstream tasks, including 3D object classification, part segmentation, point cloud completion, semantic segmentation, and detection. Additionally, our ablation studies validate the effectiveness of DR-Point in enhancing point cloud understanding.