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Found 47 papers, 25 papers with code
Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection
We present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains.
Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving
In this paper, we explore the feasibility of deriving decisions from an autoregressive world model by addressing these challenges through the formulation of multiple probabilistic hypotheses.
Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression
Slow inference speed is one of the most crucial concerns for deploying multi-view 3D detectors to tasks with high real-time requirements like autonomous driving.
LION: Linear Group RNN for 3D Object Detection in Point Clouds
To tackle this problem, we simply introduce a 3D spatial feature descriptor and integrate it into the linear group RNN operators to enhance their spatial features rather than blindly increasing the number of scanning orders for voxel features.
Ranked #1 on
3D Object Detection
on Waymo Open Dataset
Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection
This module adjusts the feature distributions from both the camera and LiDAR, bringing them closer to the ground truth domain and minimizing differences.
SEED: A Simple and Effective 3D DETR in Point Clouds
We argue that the main challenges are twofold: 1) How to obtain the appropriate object queries is challenging due to the high sparsity and uneven distribution of point clouds; 2) How to implement an effective query interaction by exploiting the rich geometric structure of point clouds is not fully explored.
OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection
Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection.
Exploring the Causality of End-to-End Autonomous Driving
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions.
BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space
The world model consists of two parts: the multi-modal tokenizer and the latent BEV sequence diffusion model.
SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation, which inspires the following core designs: a Simple Instance-aware Dense Sampling (SIDS) strategy is used to generate comprehensive dense pseudo-labels; the Geometry-aware Adaptive Weighting (GAW) loss dynamically modulates the importance of each pair between pseudo-label and corresponding prediction by leveraging the intricate geometric information of aerial objects; we treat aerial images as global layouts and explicitly build the many-to-many relationship between the sets of pseudo-labels and predictions via the proposed Noise-driven Global Consistency (NGC).
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection
Vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain, since it encompasses inherent advantages in reducing blind spots and expanding perception range.
CLIP-GS: CLIP-Informed Gaussian Splatting for Real-time and View-consistent 3D Semantic Understanding
Additionally, to address the semantic ambiguity, caused by utilizing view-inconsistent 2D CLIP semantics to supervise Gaussians, we introduce a 3D Coherent Self-training (3DCS) strategy, resorting to the multi-view consistency originated from the 3D model.
PointMamba: A Simple State Space Model for Point Cloud Analysis
Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model
To alleviate the problem, we propose a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate realistic multi-view videos controlled by 3D layout.
Forward Flow for Novel View Synthesis of Dynamic Scenes
This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping.
Diffusion-based 3D Object Detection with Random Boxes
3D object detection is an essential task for achieving autonomous driving.
SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model
In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks.
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning.
Ranked #1 on
Trajectory Planning
on nuScenes
Multi-Modal 3D Object Detection by Box Matching
Extensive experiments on the nuScenes dataset demonstrate that our method is much more stable in dealing with challenging cases such as asynchronous sensors, misaligned sensor placement, and degenerated camera images than existing fusion methods.
Ranked #48 on
3D Object Detection
on nuScenes
SOOD: Towards Semi-Supervised Oriented Object Detection
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem.
Ranked #1 on
Cross-Part Crowd Counting
on ShanghaiTech B
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories.
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.
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
In this paper, we address the problem of detecting 3D objects from multi-view images.
Ranked #9 on
3D Object Detection
on nuScenes Camera Only
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection
In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation.
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance.
Command-Driven Articulated Object Understanding and Manipulation
The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations.
Repainting and Imitating Learning for Lane Detection
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation
Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
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.
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network
A feasible method is investigated to construct conceptual scenes without external datasets.
Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network
In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference.
Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis
The second module contains a density and a color grid to model the geometry and density of the scene.
GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation
Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability.
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system.
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation
We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation.
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
SGM3D: Stereo Guided Monocular 3D Object Detection
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network
In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting
Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #14 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation
Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet.
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques.
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation
Semantic segmentation of 3D unstructured point clouds remains an open research problem.
