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Found 37 papers, 29 papers with code
VLM-Grounder: A VLM Agent for Zero-Shot 3D Visual Grounding
3D visual grounding is crucial for robots, requiring integration of natural language and 3D scene understanding.
Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation
6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image.
LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness
Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos.
GRUtopia: Dream General Robots in a City at Scale
Recent works have been exploring the scaling laws in the field of Embodied AI.
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities
Although great progress has been made in 3D visual grounding, current models still rely on explicit textual descriptions for grounding and lack the ability to reason human intentions from implicit instructions.
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Given the scarcity of motion capture data on multi-humanoid collaboration and the efficiency challenges associated with multi-agent learning, these tasks cannot be straightforwardly addressed using training paradigms designed for single-agent scenarios.
MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
With the emergence of LLMs and their integration with other data modalities, multi-modal 3D perception attracts more attention due to its connectivity to the physical world and makes rapid progress.
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments.
Grounded 3D-LLM with Referent Tokens
To facilitate the use of referent tokens in subsequent language modeling, we provide a large-scale, automatically curated grounded scene-text dataset with over 1 million phrase-to-region correspondences and introduce Contrastive Language-Scene Pre-training (CLASP) to perform phrase-level scene-text alignment using this data.
GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction
Previous works attempt to automate this process using the Next-Best-View (NBV) policy for active 3D reconstruction.
EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI
In the realm of computer vision and robotics, embodied agents are expected to explore their environment and carry out human instructions.
OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries
Occupancy prediction has increasingly garnered attention in recent years for its fine-grained understanding of 3D scenes.
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection
Instead of leveraging 2D images, we propose Object2Scene, the first approach that leverages large-scale large-vocabulary 3D object datasets to augment existing 3D scene datasets for open-vocabulary 3D object detection.
Unified Human-Scene Interaction via Prompted Chain-of-Contacts
Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution.
PointLLM: Empowering Large Language Models to Understand Point Clouds
The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding.
Ranked #3 on
3D Object Captioning
on Objaverse
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking
Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation.
Position-Guided Point Cloud Panoptic Segmentation Transformer
DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception.
Ranked #1 on
Panoptic Segmentation
on SemanticKITTI
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training
This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset.
GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding
In this paper, we propose Geometry Enhanced Masked Image Modeling (GeoMIM) to transfer the knowledge of the LiDAR model in a pretrain-finetune paradigm for improving the multi-view camera-based 3D detection.
Vision-Centric BEV Perception: A Survey
In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion.
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones
In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022.
Monocular 3D Object Detection with Depth from Motion
Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings.
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
In this paper, we introduce the first DETR framework for Monocular DEtection with a depth-guided TRansformer, named MonoDETR.
Ranked #10 on
3D Object Detection From Monocular Images
on KITTI-360
3D Object Detection From Monocular Images
Autonomous Driving
+4
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception
In this paper, we benchmark our model on these three tasks.
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation
Due to the information sparsity of local cost volume, we further introduce match reweighting and structure-aware attention, to make the depth information more concentrated.
Probabilistic and Geometric Depth: Detecting Objects in Perspective
As the preliminary depth estimation of each instance is usually inaccurate in this ill-posed setting, we incorporate a probabilistic representation to capture the uncertainty.
Ranked #10 on
3D Object Detection
on KITTI Cars Hard val
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D.
Ranked #327 on
3D Object Detection
on nuScenes
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
Ranked #3 on
3D Semantic Segmentation
on ScribbleKITTI
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds.
Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds
LiDAR is an important method for autonomous driving systems to sense the environment.
An Empirical Study on Academic Commentary and Its Implications on Reading and Writing
The relationship between reading and writing (RRW) is one of the major themes in learning science.
