Search Results for author:
Found 14 papers, 11 papers with code
CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians
The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS).
Segment Anything in 3D Gaussians
In this paper, we propose a novel approach to achieve object segmentation in 3D Gaussian via an interactive procedure without any training process and learned parameters.
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection
Thus, MixSup leverages massive coarse cluster-level labels to learn semantics and a few expensive box-level labels to learn accurate poses and shapes.
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving
In autonomous driving, predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to better plan their actions, enhancing safety and efficiency on the road.
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels
Consequently, we develop a suite of components to complement the virtual voxel concept, including a virtual voxel encoder, a virtual voxel mixer, and a virtual voxel assignment strategy.
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding.
Tracking Objects with 3D Representation from Videos
In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.
Fully Sparse Fusion for 3D Object Detection
In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture.
Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection
Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective.
Super Sparse 3D Object Detection
To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD.
Fully Sparse 3D Object Detection
To enable efficient long-range LiDAR-based object detection, we build a fully sparse 3D object detector (FSD).
Embracing Single Stride 3D Object Detector with Sparse Transformer
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #3 on
3D Object Detection
on waymo cyclist
RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection
The most notable difference with previous works is that our method is purely based on the range view representation.
RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection
We first analyze the existing range-view-based methods and find two issues overlooked by previous works: 1) the scale variation between nearby and far away objects; 2) the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinates used in output.
