[Paper Review] Ponder: Point Cloud Pre-training via Neural Rendering

Di Huang, Sida Peng+4 more|arXiv (Cornell University)|Dec 31, 2022

3D Shape Modeling and Analysis4 citations

TL;DR

Ponder proposes a novel self-supervised pre-training method for 3D point clouds using differentiable neural rendering, where a point cloud encoder is trained to reconstruct real RGB-D images from learned 3D features. The approach achieves state-of-the-art performance across diverse 3D tasks, including 3D detection, segmentation, reconstruction, and image synthesis, with consistent improvements over existing contrastive and completion-based methods.

ABSTRACT

We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised point-based neural renderer by comparing the rendered images with real images on massive RGB-D data. The learned point-cloud encoder can be easily integrated into various downstream tasks, including not only high-level tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image synthesis. Extensive experiments on various tasks demonstrate the superiority of our approach compared to existing pre-training methods.

Motivation & Objective

• To address the scarcity of annotated 3D point cloud data by developing a self-supervised pre-training method that leverages abundant RGB-D data.
• To improve 3D representation learning by enforcing point cloud features to encode rich geometry and appearance cues through neural rendering.
• To enable transferability of pre-trained features to both high-level perception tasks (e.g., detection, segmentation) and low-level tasks (e.g., reconstruction, image synthesis).
• To explore a new pretext task—neural rendering reconstruction—distinct from contrastive or completion-based pre-training.
• To validate the effectiveness of the method on diverse datasets and modalities, including outdoor scenes and multiview inputs.

Proposed method

• A 3D point cloud encoder processes input RGB-D images to learn geometry and appearance features, forming a neural scene representation.
• The learned 3D features are rendered into 2D RGB and depth images using a differentiable neural renderer with camera parameters.
• The model is trained end-to-end by minimizing the L1 and L2 loss between rendered and real images, using a multi-view supervision strategy.
• The method constructs a 3D feature volume with resolutions of [16, 32, 64], enabling multi-scale feature learning.
• The framework supports both point-based and voxel-based backbones, allowing flexible integration into various 3D models.
• The pre-trained model can be fine-tuned for downstream tasks or directly used for 3D reconstruction and image synthesis via SDF querying and Marching Cubes.

Experimental results

Research questions

• RQ1 Can differentiable neural rendering serve as an effective pretext task for self-supervised 3D point cloud representation learning?
• RQ2 Does pre-training via neural rendering lead to better generalization on both high-level and low-level 3D vision tasks compared to contrastive or completion-based methods?
• RQ3 How does multi-view supervision influence the quality of learned 3D representations and downstream performance?
• RQ4 To what extent can the pre-trained model generalize to sparse input point clouds and complex scenes?
• RQ5 Can the method be effectively extended to other modalities, such as multiview images or outdoor datasets?

Key findings

• Ponder achieves a 33.5% AP50 on ScanNet for 3D detection using only RGB-D supervision, improving by +7.5% over the baseline.
• Combining depth and color supervision yields the best performance, with a +7.5% AP50 gain on ScanNet and +3.7% on SUN RGB-D.
• Even with a minimal 16×16×16 feature volume resolution, Ponder achieves competitive performance, indicating robustness to resolution reduction.
• Using five input views improves AP50 by 0.9% on ScanNet and 1.2% on SUN RGB-D compared to single-view supervision.
• The method enables high-fidelity 3D reconstruction from sparse point clouds (2% of original points), producing detailed meshes via SDF extraction and Marching Cubes.
• On the NuScenes dataset using multiview images, Ponder achieves a 1.41% mAP improvement in 3D multiview object detection, demonstrating cross-modality transferability.