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Simon Fraser University
- Vancouver, Canada
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05:22
- 7h behind - https://orcid.org/0000-0002-2739-5298
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Official code of DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning
NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
Official repository for "AM-RADIO: Reduce All Domains Into One"
[NeurIPS 2024] Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding
Productive, portable, and performant GPU programming in Python.
[CVPR 2025] Align3R: Aligned Monocular Depth Estimation for Dynamic Videos
[ICLR 2025] SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
Official repo for paper "Structured 3D Latents for Scalable and Versatile 3D Generation" (CVPR'25).
Diorama: Unleashing Zero-shot Single-view 3D Scene Modeling
Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
This repo contains the code for "MEGA-Bench Scaling Multimodal Evaluation to over 500 Real-World Tasks" [ICLR2025]
[3DV 2025] Official Implementation of the paper "SceneMotifCoder: Example-driven Visual Program Learning for Generating 3D Object Arrangements"
We present Object Images (Omages): An homage to the classic Geometry Images.
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
Code for paper "PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify" [ICLR2025]
[ICLR 2025] Duoduo CLIP: Efficient 3D Understanding with Multi-View Images
[CVPR 2024] Probing the 3D Awareness of Visual Foundation Models
Code repository for the Habitat Synthetic Scenes Dataset (HSSD) paper.
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.
Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).