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anagh malik anagh malik i am a fourth year computer science phd student at the university of toronto supervised by david lindell i am affiliated with both the toronto computational imaging group and the vector institute my work has been previously recognized with the cvpr 2025 best student paper award previously i completed my mres at the dyson robotics lab at imperial college london where i worked on self supervised segmentation under the supervision of andrew davison and ronald clark email anagh at cs dot toronto dot edu twitter nbsp nbsp resume nbsp nbsp google scholar nbsp nbsp github news 2026 05 velox and dark3r accepted to cvpr 2026 2026 01 i was featured in a veritasium video discussing flying with photons 2025 09 i was featured in the executive code podcast 2025 06 neural inverse rendering from propagating light won the best student paper award at cvpr 2025 2025 06 organizing the physics inspired 3d vision and imaging pi3dvi workshop at cvpr 2025 2025 04 gave talks at the stanford graphics seminar and the stanford computational imaging lab 2025 04 neural inverse rendering from propagating light accepted to cvpr 2025 as an oral 2025 03 started an internship at apple machine learning research in cupertino 2024 09 flying with photons accepted to eccv 2024 as an oral 2023 10 transientnerf accepted to neurips 2023 as a spotlight research i am broadly interested in scene understanding that is using visual cues to infer properites of objects and scenes your browser does not support the video tag velox learning representations of 4d geometry and appearance anagh malik dorian chan xiaoming zhao david b lindell oncel tuzel jen hao rick chang cvpr 2026 project page arxiv code a framework for learning compact latent representations of dynamic 4d objects from unstructured point clouds enabling video to 4d generation cloth simulation and 3d point tracking your browser does not support the video tag dark3r learning structure from motion in the dark andrew y guo anagh malik saikiran tedla yutong dai yiqian qin zach salahe benjamin attal sotiris nousias kiriakos n kutulakos david b lindell cvpr 2026 nbsp highlight project page arxiv code recovers camera pose and depth maps from noisy low light raw images for dense neural reconstruction enabling accurate 3d scene recovery in extremely low light conditions your browser does not support the video tag neural inverse rendering from propagating light anagh malik benjamin attal andrew xie matthew o toole david b lindell cvpr 2025 nbsp oral presentation best student paper award project page arxiv code time resolved relighting and geometry estimation through radiance caching your browser does not support the video tag opportunistic single photon time of flight sotiris nousias mian wei howard xiao maxx wu shahmeer athar kevin j wang anagh malik david a barmherzig david b lindell kiriakos n kutulakos cvpr 2025 nbsp oral presentation project page we propose a passive single photon method that opportunistically recovers time of flight from ambient pulsed light sources your browser does not support the video tag flying with photons rendering novel views of propagating light anagh malik noah juravsky ryan po gordon wetzstein kiriakos n kutulakos david b lindell eccv 2024 nbsp oral presentation project page video arxiv code novel view synthesis of arbitrary light propagation videos including effects such as scattering or interreflections your browser does not support the video tag transient neural radiance fields for lidar view synthesis and 3d reconstruction anagh malik parsa mirdehghan sotiris nousias kiriakos n kutulakos david b lindell neurips 2023 nbsp spotlight project page video arxiv code we introduce a method to do novel view lidar synthesis allowing sparse view scene reconstruction exploring neural representations for self supervised segmentation anagh malik master s thesis 2022 we develop a method for self supervised segmentation through agreement and self distillation segdip the unreasonable effectiveness of randomly initialized cnns for interactive segmentation anagh malik shuaifeng zhi marwan taher ronald clark andrew davison technical report 2021 we train an encoder decoder network to map from xy coordinates to rgb values and semantic classes allowing real time segmentation of an image template stolen from jon barron s website
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