Search Results for author:
Found 9 papers, 8 papers with code
TEXGen: a Generative Diffusion Model for Mesh Textures
Instead, we focus on the fundamental problem of learning in the UV texture space itself.
MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More
Our MC-MoE integrates static quantization and dynamic pruning to collaboratively achieve extreme compression for MoE-LLMs with less accuracy loss, ensuring an optimal trade-off between performance and efficiency.
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories.
IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation
Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category.
Spherical Transformer for LiDAR-based 3D Recognition
In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones.
Ranked #1 on
Semantic Segmentation
on KITTI Semantic Segmentation
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies.
Ranked #1 on
3D Multi-Object Tracking
on nuScenes LiDAR only
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects.
LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs
Recent advance in 2D CNNs has revealed that large kernels are important.
Stratified Transformer for 3D Point Cloud Segmentation
In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.
Ranked #23 on
Semantic Segmentation
on ScanNet
