3D

AToM: Amortized Text-to-Mesh using 2D Diffusion

We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly …

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

We present “Magic123”, a two-stage coarse-to-fine solution for high-quality, tex-tured 3D meshes generation from a single unposed image in the wild using both 2D and 3D priors. In the first stage, we optimize a coarse neural radiance field and focus …

Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding

While Transformers have achieved impressive success in natural language processing and computer vision, their performance on 3D point clouds is relatively poor. This is mainly due to the limitation of Transformers: a demanding need for extensive …

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of …

ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning

Access to 3D point cloud representations has been widely facilitated by LiDAR sensors embedded in various mobile devices. This has led to an emerging needfor fast and accurate point cloud processing techniques. In this paper, we revisitand dive …

DeepGCNs: Making GCNs Go as Deep as CNNs

Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for …

PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs) to better …

SGAS: Sequential Greedy Architecture Search

Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. …