Search Results for author:
Found 17 papers, 5 papers with code
Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles
We demonstrate in a simulation system that such inference time attacks can also threaten the safety of both the ego vehicle and other traffic participants.
FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction
After the feed-forward reconstruction of 3DGS primitives, we investigate a depth-regularized per-scene fine-tuning process.
ChatSplat: 3D Conversational Gaussian Splatting
Extensive experimental results demonstrate that ChatSplat supports multi-level interactions -- object, view, and scene -- within 3D space, enhancing both understanding and engagement.
Generalizable Human Gaussians from Single-View Image
To mitigate the potential generation of unrealistic human poses and shapes, we incorporate human priors from the SMPL-X model as a dual branch, propagating image features from the SMPL-X volume to the image Gaussians using sparse convolution and attention mechanisms.
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation.
FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes
However, existing generalizable 3D Gaussian Splatting methods are largely confined to narrow-range interpolation between stereo images due to their heavy backbones, thus lacking the ability to accurately localize 3D Gaussian and support free-view synthesis across wide view range.
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields
Recent advancements in vision-language foundation models have significantly enhanced open-vocabulary 3D scene understanding.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
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NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.
Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories
Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information.
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection
Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs).
UNIKD: UNcertainty-filtered Incremental Knowledge Distillation for Neural Implicit Representation
In view of this, we explore the task of incremental learning for NIRs in this work.
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search
Based on these new discoveries, we propose i) a novel hybrid zero-shot proxy which outperforms existing ones by a large margin and is transferable among popular search spaces; ii) a new index for better measuring the true performance of ZS-NAS proxies in constrained NAS.
Binarized Neural Architecture Search for Efficient Object Recognition
In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.
Cogradient Descent for Bilinear Optimization
Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).
Binarized Neural Architecture Search
A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.
