Search Results for author:
Found 9 papers, 9 papers with code
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
Spatial intelligence is a critical component of embodied AI, promoting robots to understand and interact with their environments.
Ranked #1 on
Spatial Reasoning
on EmbSpatial-Bench
Positional Prompt Tuning for Efficient 3D Representation Learning
Together with the sequential Transformer, the whole module with position encoding comprehensively constructs a multi-scale feature abstraction module that considers both the local parts from the patch and the global parts from center points as position encoding.
3D Parameter-Efficient Fine-Tuning for Classification
3D Point Cloud Classification
+4
DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
Ranked #1 on
3D Question Answering (3D-QA)
on 3D MM-Vet
DreamLLM: Synergistic Multimodal Comprehension and Creation
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation.
Ranked #5 on
Visual Question Answering
on MMBench
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
VPP leverages structured voxel representation in the proposed Voxel Semantic Generator and the sparsity of unstructured point representation in the Point Upsampler, enabling efficient generation of multi-category objects.
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
Geometry and color information provided by the point clouds are both crucial for 3D scene understanding.
Ranked #1 on
Unsupervised 3D Semantic Segmentation
on ScanNetV2
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
Ranked #1 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet10
(using extra training data)
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
Ranked #8 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (10-shot)
(using extra training data)
Few-Shot 3D Point Cloud Classification
Knowledge Distillation
+1
