MADrive: Memory-Augmented Driving Scene Modeling
Recent advances in scene reconstruction have pushed toward highly realistic
modeling of autonomous driving (AD) environments using 3D Gaussian splatting.
However, the resulting reconstructions remain closely tied to the original
observations and struggle to support photorealistic synthesis of significantly
altered or novel driving scenarios. This work introduces MADrive, a
memory-augmented reconstruction framework designed to extend the capabilities
of existing scene reconstruction methods by replacing observed vehicles with
visually similar 3D assets retrieved from a large-scale external memory bank.
Specifically, we release MAD-Cars, a curated dataset of {sim}70K 360{\deg}
car videos captured in the wild and present a retrieval module that finds the
most similar car instances in the memory bank, reconstructs the corresponding
3D assets from video, and integrates them into the target scene through
orientation alignment and relighting. The resulting replacements provide
complete multi-view representations of vehicles in the scene, enabling
photorealistic synthesis of substantially altered configurations, as
demonstrated in our experiments. Project page:
https://yandex-research.github.io/madrive/
685e61f671131fa43be08b80
Polina Karpikova
685e61f671131fa43be08b81
Daniil Selikhanovych
claimed_verified
2025-06-27T10:21:04.685Z
64a42977250bfdecd9570a9e
/avatars/df5d7cf159e6bb9e961e1c77d1b89d36.svg
Daniil Selikhanovych
user
apryc1
685e61f671131fa43be08b82
Kirill Struminsky
685e61f671131fa43be08b83
Ruslan Musaev
claimed_verified
2025-06-27T12:57:53.106Z
67dc355c829f818b4b6b64b6
/avatars/2795a0e2d8136640da4bc439135dadb4.svg
Ruslan Musaev
user
Rusamus19
685e61f671131fa43be08b84
Maria Golitsyna
685e61f671131fa43be08b85
Dmitry Baranchuk
MADrive: Memory-Augmented Driving Scene Modeling
Recent advances in scene reconstruction have pushed toward highly realistic
modeling of autonomous driving (AD) environments using 3D Gaussian splatting.
However, the resulting reconstructions remain closely tied to the original
observations and struggle to support photorealistic synthesis of significantly
altered or novel driving scenarios. This work introduces MADrive, a
memory-augmented reconstruction framework designed to extend the capabilities
of existing scene reconstruction methods by replacing observed vehicles with
visually similar 3D assets retrieved from a large-scale external memory bank.
Specifically, we release MAD-Cars, a curated dataset of {sim}70K 360{\deg}
car videos captured in the wild and present a retrieval module that finds the
most similar car instances in the memory bank, reconstructs the corresponding
3D assets from video, and integrates them into the target scene through
orientation alignment and relighting. The resulting replacements provide
complete multi-view representations of vehicles in the scene, enabling
photorealistic synthesis of substantially altered configurations, as
demonstrated in our experiments. Project page:
https://yandex-research.github.io/madrive/
MADrive enhances scene reconstruction for autonomous driving by integrating visually similar 3D car assets from an external memory bank to achieve photorealistic synthesis of altered scenarios.
3D Gaussian splatting scene reconstruction memory-augmented reconstruction MADrive MAD-Cars 360° car videos retrieval module 3D asset reconstruction orientation alignment relighting