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

TL;DR

DR-Point addresses the data scarcity and limited modality coverage in 3D understanding by proposing tri-modal pre-training that unifies RGB, depth, and point-cloud representations. It innovations include differentiable rendering to synthesize depth images and enhance point-cloud reconstruction, and a tri-branch Transformer-based encoder with cross-modal MoCo-style contrastive learning, yielding a comprehensive loss $L_{total}$ that combines $L_{(R, D)}$, $L_{(R, P)}$, $L_{(P, D)}$, $L_{MoCo}$, $L_{CE}$, $L_{DR}$, and $L_{CD}$ with coefficients $(\,\alpha,\beta,\theta)$. The approach demonstrates strong improvements across 3D object classification, part segmentation, indoor semantic segmentation, object detection, and point cloud completion on diverse datasets, with ablations validating the benefit of tri-modal alignment and differentiable depth rendering. The work suggests substantial practical impact for data-efficient 3D understanding in robotics, AR/VR, and autonomous systems, and opens avenues for cross-modal retrieval leveraging unified tri-modal representations. The overall objective can be written as $L_{total}=\alpha L_{(R, D)}+\beta L_{(R, P)}+\theta L_{(P, D)}+L_{MoCo}+L_{CE}+L_{DR}+L_{CD}$, where $\alpha,\beta,\theta=0.1$ in the reported setup.

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.

Figures (9)

• Figure 1: Illustrations of DR-Point, a methodology for improving the 3D understanding by aligning features from tri-modalities, such as RGB images, depth images, and point clouds into a shared space. DR-Point aims to reduce the requirement of object triplets using Differentiable Rendering to obtain depth images, together with RGB images and point clouds from image-3D pairs to enhance the representative learning of models.
• Figure 2: Illustration of DR-Point. The tri-modal pre-training of DR-Point requires a batch of objects represented as triplets (RGB image, depth image, point cloud), which are extracted from three branches: (i) Token-level Transformer Auto-encoder (Top) aims to recover point clouds at the token level as well as exploit 3D features; (ii) Point-level Transformer Auto-encoder (Middle) is designed to reconstruct point clouds at the point-level, which shares the Transformer encoder with the former branch. Moreover, differentiable rendering is leveraged to ensure the reconstruction of high-quality point clouds from 32 random views, while one random depth view will be leveraged to exploit depth features; (iii) RGB features (Bottom) are extracted from a pre-trained ResNet with a projection head. During pre-training, contrastive losses are applied to align the 3D feature of an object with its corresponding RGB and depth features.
• Figure 3: The pipeline of differentiable point cloud renderer.
• Figure 4: Visualization comparison of segmentation on ShapeNetPart.
• Figure 5: Visualization comparisons on PCN dataset, which is the commonly used point cloud completion dataset.