Search Results for author:
Found 8 papers, 2 papers with code
Interactive3D: Create What You Want by Interactive 3D Generation
Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process, restricting the scope for direct and versatile 3D modifications.
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection
To mitigate the computational complexity associated with applying a window-based transformer in 3D voxel space, we introduce a novel Chessboard Sampling strategy and implement voxel sampling and gathering operations sparsely using a hash map.
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets.
Obj-NeRF: Extract Object NeRFs from Multi-view Images
To solve this problem, in this paper, we propose Obj-NeRF, a comprehensive pipeline that recovers the 3D geometry of a specific object from multi-view images using a single prompt.
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Robust 3D perception under corruption has become an essential task for the realm of 3D vision.
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Ranked #1 on
3D Object Detection
on SUN-RGBD
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement.
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds.
